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1. General comments

Two specific trends can be noticed in modern chemical analysis. One is the continuous demand for more sensitive and accurate analytical methods. The other is the desire for simpler methods that require as little as possible human intervention. One of the various procedures to make the analytical methods more sensitive and accurate is the use of specific chemical changes (e.g., derivatization) applied on the analytes or even on the whole sample. However, these changes frequently involve more human intervention than the direct use of advanced instrumentation. For this reason, the methods involving chemical changes such as derivatizations are not necessarily the first choice when selecting an analytical method. Nevertheless, in many cases, the benefits of derivatization are more important than the disadvantage of requiring human intervention, and for this reason, derivatization is still frequently used in the analytical practice. Also, modern GC, GC/MS (or GC/MS/MS) instrumentation may offer autosampling with the capability of adding reagents to the sample, as well as stirring, heating, and injecting the sample at specific time intervals in the GC system. This type of instrumentation may reduce significantly the human handling involved in derivatization.

Various chemical changes can be performed on an analyte in order to make it suitable for a specific method of analysis. The most common is derivatization, but other chemical changes can be utilized, for example, pyrolytic decomposition and, in the case of polymers, polymer fragmentation using reagents. The choice depends on the nature of the analyte, the sample matrix, the intended changes in the analyte properties, and the analytical method to be used.

The addition of a reagent on a sample may produce a chemical reaction only with the analytes without affecting the matrix. However, it is also possible that some matrix components are derivatized unintentionally. Usually, it is preferable to have only the analytes derivatized since in this way a better separation from the matrix is expected. Some derivatizations are used in the sample cleanup or concentration process. Also, the derivatization process may be combined with simultaneous extraction and concentration of the sample or may be followed by a second preparation step before the chromatographic analysis. More frequently, the derivatization is done to change the analyte properties for the core analytical procedure (GC, GC/MS, etc.).

Derivatization can be applied before the core chromatographic process or after it. Precolumn derivatization takes place before the separation and postcolumn derivatization after it. In GC precolumn derivatization is much more common and most derivatizations are performed "offline." There are however derivatizations that can be done "online," for example, in the injection port of the GC such as some methylations using tetramethyl ammonium hydroxide (TMAH). Postcolumn derivatizations are performed only for enhancing the detectability of the analytes. Typically, they must be done "online" and should be completed in the specific time frame needed by the analyte to reach the detector.

A wide variety of derivatization reagents and procedures are described in the literature, with the reagents carrying specific moieties that provide a desired property to the analytes, as well as with specific reactive groups that permit the reaction with the analyte. Multiple step derivatizations as well as derivatizations followed by a second one are known.

Derivatization is not always the first step in sample preparation. Sample preparation typically includes other operations, besides derivatization. Some of these steps are more complex such as sample cleanup or concentration and others more simple such as pH adjustments, addition of proton acceptors or donors, change of the medium (from one solvent to another), and addition of catalysts to enhance the derivatization, and these may be necessary for a successful derivatization.

Although derivatization is performed in order to make possible or to improve the results of a chemical analysis, there are also some disadvantages of using derivatization. Besides the potential need of more manpower for the analysis, the addition of more operations applied on the sample (including the analytes) can be a source of additional errors. In particular the involvement of a chemical reaction that may not be perfectly controlled can bring significant errors in the analytical results. To minimize the potential errors when using derivatization, specific aspects of the derivatization must be considered in its choice, such as the efficiency of the chemical reaction used in the derivatization, the stability of the derivatized analytes, the availability of reagents and necessary equipment, and the time necessary for performing the analysis. For a given analyte or group of analytes, the reaction with the derivatization reagent must be complete or at least close to complete, must take place in a length of time that is not prohibitive, and must have very little loss of the analyte with formation of artifacts or decomposition products. Only when such criteria are satisfied can a specific chosen derivatization be applied successfully.

The application of derivatization in chromatography is the subject of many studies. Numerous derivatizations have been reported in journals (e.g., J. Chromatogr. A and B, J. Chromatogr. Sci., J. Sep. Sci., Chromatographia, etc.), in various books [ 1, 2, 3, 4, 5], in application notes of instrument manufacturers, as well as on the web.

2. Derivatization for improving separation in gas chromatography

For GC analysis, the effect of derivatization can be beneficial in a variety of circumstances. Some of the most common uses of derivatization for improving the GC separation are the following:

(a) Derivatization that replaces active (polar) hydrogen atoms in the analyte to decrease its boiling point. The active hydrogens in a chemical compound typically enhance the capability to form hydrogen bonds and increase the compound polarity. For this reason, many compounds containing active (polar) hydrogens are not volatile, the volatility being necessary for using GC or GC/MS as a core analytical method. Derivatization can be used to replace active hydrogens from an analyte Y-H (or Y: H) in functional groups such as OH, COOH, SH, NH, and CONH. These reactions can be written in a simplified form as follows:

In reaction (1), the reagent R-X contains an "active" group X and a group R that carries a desired property (e.g., lack of polarity for GC). Group R in the reagent can be a low molecular mass fragment such as CH ₃ or C ₂H ₅, a short-chain fluorinated alkyl in alkylation reactions, Si(CH ₃) ₃ or other silyl groups in silylations, COCH ₃ or short-chain fluorinated acyl groups in acylations, etc. An example of a chromatogram resulting from the GC/MS analysis of a silylated tobacco sample is given in Figure 1. Tobacco contains many hydroxy acids such as malic, trihydroxybutanoic, citric, quinic, glucuronic, and chlorogenic. Also, it contains monosaccharides (e.g., glucose, fructose), disaccharides (e.g., sucrose), and even trisaccharides. None of these compounds are volatile, having numerous active hydrogens. The replacement of these hydrogens with Si(CH ₃) ₃ by silylation renders these compounds volatile, and they can be analyzed by GC/MS as seen in Figure 1.

1. (b) Derivatization for enhancing the separation. Specific moieties added to an analyte may be necessary for enhancing the separation. This is frequently practiced for general GC separations and is also very useful for the separation of chiral molecules (see Section 4). The derivatized analytes may have significantly different properties from each other, for example, regarding polarity and implicitly in their boiling point, allowing separations that are difficult to achieve otherwise. Also, derivatization may generate more significant differences between the analytes and the matrix components.

2. (c) Derivatization that replaces active hydrogens in the analyte to improve the behavior of the analyte in the chromatographic separation. The chromatographic column (e.g., a capillary column coated with a bonded stationary phase) may display additional capability to interact with polar molecules, besides the intended interactions due to its bonded phase. This may come, for example, from the silica wall of the column. Secondary interactions taking place with only a portion of the molecules of the analyte generate peak tailing. This is exemplified in Figure 2 which shows a hypothetical case of two different types of interaction between the column and a specific molecular species.

(d) Derivatization for the improvement of stability of a compound. This stability may refer to thermal stability, a property which overlaps to a certain extent to what was described at point (a). However, even some volatile compounds may be further thermally stabilized by derivatization. Also, chemical stability can be enhanced by protecting specific groups in the analyte using derivatization. For example, thiols can be protected using derivatization against oxidation by the traces of oxygen in the heated injection port of the GC.

The choice of the appropriate derivatization is not always a simple task. The replacement of a hydrogen atom with a group of atoms may increase the molecular weight of the derivatized analyte. In such cases, it must be verified that the increase in the molecular weight by derivatization brings no or only a small increase in the boiling point of the analyte. Most of the time, low molecular weight substituents such as CH ₃ or Si(CH ₃) ₃ are preferable for GC analysis to the active hydrogens for achieving the previously described goals. Large substituents may increase the boiling point too much and make the compound not acceptable for GC analysis.

Besides replacement of active hydrogens, other derivatization reactions can be utilized. For example, condensation reactions may decrease the boiling point and improve the thermal stability of an analyte. However, the generation of new active hydrogens must be avoided in condensation reactions or must be followed by a second derivatization.

3. Derivatization for chiral separation in gas chromatography

The compounds with structures that are mirror images to each other are indicated as enantiomers, and their molecules are not superimposable, having the property called chirality. Chirality is commonly caused by the existence in the molecule of at least one tetrahedral carbon atom substituted with groups that are different. However, chiral molecules may be generated with a phosphorus or a sulfur chiral atom. Not only chiral centers (such as an asymmetric carbon) generate enantiomers, but a chiral axis or a chiral plane can lead to enantiomers. The chirality in an enantiomer is specified using the symbols R and S based on specific rules. For the assignment of a symbol R or S to a chiral carbon, the substituents are arranged in a sequence a > b > c > d. For the four atoms directly attached to the asymmetric carbon, a higher atomic number outranks the lower, and a higher atomic mass outranks the lower mass. For the same atoms directly attached to the asymmetric carbon, the priorities are assigned at the first point of difference. After the sequence is established, the molecule is oriented in space with the group "d" of the lowest priority behind the asymmetric carbon. When viewed along the C─d bond (from C) and the three substituents a, b, and c are oriented clockwise, the compound contains an R asymmetric carbon, and it contains an S asymmetric carbon for counterclockwise arrangement.

More than one asymmetric carbon can be present in a molecule, as in the case of carbohydrates. The stereoisomers generated by more than one asymmetric carbon can be mirror image one to the other (enantiomers) or may have different steric arrangements being diastereoisomers. These types of molecules are schematically shown in Figure 3.

The (S,S)- and the (R,R)-compounds from Figure 3 are enantiomers, while the (S,R)-compound is a diastereoisomer to both (S,S)- and to (R,R)-compounds (it is an enantiomer to the (R,S)-compound). The gas chromatographic separation of enantiomers can be done only using chromatographic columns having chiral stationary phases. The derivatization of enantiomers with non-chiral reagents generates molecules that remain enantiomers. This type of derivatization may improve the chromatographic separation from other molecules, but the derivatized compounds of remaining enantiomers cannot be separated except on chiral stationary phases. Sometimes, better separation can be obtained even between the enantiomers (on chiral chromatographic columns) after derivatization. One such example is the separation of (R)- and (S)-nornicotine derivatized with isobutyl chloroformate on a chiral Rt-BDEXsm column with separation improved compared to that of underivatized enantiomers [ 6]. The derivatization reaction is indicated below:

Diastereoisomers can be separated on chromatographic columns with non-chiral stationary phases which offer a much wider possibility to select the column. For this reason, an alternative procedure toward the separation of enantiomers is using derivatization with chiral reagents. This type of derivatization generates diastereoisomers which can be separated on non-chiral stationary phases.

A discussion on the separation of enantiomers on chiral phases without derivatization is beyond the purpose of this chapter. Numerous publications are dedicated to this subject, including papers published in general chromatography journals or in dedicated journals (e.g., Chirality), books (see, e.g., [ 7]), and information on the web.

The separation after derivatization with a pure enantiomer reagent is based on formation of diastereoisomers that can be separated on regular stationary phases. Depending on the nature of the analyte and of the derivatization, different separation techniques can be applied. A variety of common columns are used for such GC separations. The choice of the column is again dependent on the analyte and the derivatization procedure. For example, α-substituted organic acids such as α-chloropropionic, α-bromocaproic, etc. can be derivatized with a specific enantiomer of an amino acid ester (e.g., ethyl 2-aminopropanoate) in the presence of a peptide coupling reagent (benzotriazol-1-yl-oxy-tris(dimethylamino)-phosphonium hexafluorophosphate or BOP) in a reaction of the type:

The derivatized acids that are now diastereoisomers (R,S) and (S,S) can be separated on a common capillary column (e.g., a DB-1701 column from Agilent). Another example of derivatization with a chiral reagent is that of methamphetamines with (R)-menthyl chloroformate. This derivatization allows the separation of over-the-counter (R)-methamphetamine from the illicit (S)-methamphetamine. The reaction of the (R)-enantiomer is indicated below [ 8]:

The separation of the (R,R) and (S,R) derivatives was possible on a non-chiral column for a GC/MS analysis.

4. Derivatization for improving gas chromatographic detection with other detectors than MS

Gas chromatography (not coupled with mass spectrometry, GC/MS being separately presented) used as an analytical technique can involve various detectors. The variety of such detectors is rather large, and several types include the following: thermal conductivity detector (TCD), flame ionization detector (FID), nitrogen-phosphorus detector (NPD), electron capture detector (ECD), flame photometric detector (FPD), photoionization detector (PID), electrolytic conductivity (Hall), sulfur chemiluminescence, nitrogen chemiluminescence, aroyl luminescence detector (ALD), atomic emission detector (AED), helium ionization detector (HID), vacuum ultraviolet (VUV) absorbance, infrared Doppler (IRD) absorption, FID with catalytic conversion of all analytes in CH ₄ (e.g., Polyarc system [ 9]), etc. The derivatization with the purpose of improving detectability in GC is determined by the type of detector utilized. Most derivatizations are performed precolumn, even if they are applied only with the purpose of improving detection. However, it is important that the derivatization for improving detection does not deteriorate the separation. Preferably, both the detection and the chromatographic separation are improved by the same derivatization. Some specific postcolumn reactions applied to the analytes are part of certain types of detectors such as chemiluminescence detectors, atomic emission detectors (AED), and FID with catalytic conversion into CH ₄. Some of these chemical changes in the analytes are not necessarily classified as derivatization reactions.

No specific derivatization is usually recommended to improve sensitivity when using nonselective detectors such as TCD and FID. However, in some cases when the detector is not sensitive to a specific analyte, such as formaldehyde or heavily halogenated compounds, derivatization can be used to enhance detection.

In case of NPD detector, derivatization with nitrogenous compounds can be done, which should give a higher sensitivity. However, this type of derivatization is not very common. An adverse result occurs for the NPD detectors when silylation is performed on the sample. Besides a possible reduction in the NPD response on silylated compounds containing nitrogen, a drastic decrease in the lifetime of the detector may occur, probably due to the excess of silylating reagent that commonly is injected with a derivatized sample and affects the alkali active element of the NPD.

The response of the photoionization detector (PID) depends on the ionization potential of the analyte, and compounds with higher ionization potential are not sensitive in PID, while those with lower ionization potential may have excellent sensitivity, as low as 10 −12 mg of sample. A derivatization resulting in lowering the ionization potential of the analyte may be beneficial for PID detection. However, derivatization for enhancing PID response is not frequently used.

Some detectors such as electron capture detectors (ECD) may benefit very much from certain derivatization types. ECD (as well as negative chemical ionization mass spectrometry or NCI-MS) can be extremely sensitive, but they are selective to compounds that are able to form more stable negative ions. ECD, for example, can have sensitivity as low as 10 −13 mg of analyte in the detector compared to the best sensitivity of FID that can be 10 −8 to 10 −11 mg of analyte. The efficiency of the process seems to be related to the ease of attaching an electron on the molecule. In ECD this process can be written as follows:

With some exceptions, ECD response can be correlated with the electron affinity of the analyte [ 4]. In general, the halogen substituents increase the sensitivity in ECD in the order I > Br > Cl > F. Multiple substitutions seem to have a cumulative effect. Besides halogens, nitro groups seem to have an effect similar to chlorine groups. For aromatic compounds, the substituents affect the sensitivity of the ECD according to their electron withdrawing capability. Strong electron withdrawing groups such as NO ₂ increase the sensitivity of the detection, while electron donating groups reduce it.

A variety of substitution groups containing electronegative elements (halogens) or nitro groups can be attached to an analyte. The procedure to attach these groups is in most cases the typical substitution of an active hydrogen in the analyte Y-H with a group R from a reagent R-X that has the appropriate active X group. Some groups used for enhancing ECD (as well as NCI-MS) sensitivity following an alkylation or aryl derivatization reaction are shown in Figure 4, and several substitution groups introduced by acylation, chloroformylation, or sulfonation used for the same purpose are shown in Figure 5. Besides alkylation or aryl derivatization, other derivatization techniques used to replace an active hydrogen are applied to introduce into a molecule as a substituent containing halogens or nitro groups enhancing significantly the detectability of the derivatized analytes by ECD (as well as NCI-MS). Silylation, for example, can be used for this purpose when silyl groups used for derivatization contain halogens. Several silyl groups containing halogens that can be attached to an analyte by silylation with special reagents are given in Figure 6 [ 4].

5. Derivatization for improving GC/MS qualitative and quantitative analysis

The most powerful tool used for compound identification purposes is very likely mass spectrometry (spectroscopy). This technique is capable to provide information from very low amounts of material such as that eluting from a chromatographic column and can be easily coupled with a gas chromatograph. Most analyses performed with MS detection (GC/MS or GC/MS/MS) are using EI+ ionization mode with electron impact at 70 eV. The electrons interact with the molecule A to eject an additional electron leaving a positively charged species (with an odd number of electrons) of the type A ▪+. The ions also receive energy during electron impact and the excess of energy induces fragmentation. For most molecules, this process can be written as follows:

The fragments B i⁺ are commonly but not always with an even number of electrons. The formation of molecular ions takes place with a range of internal energies, and more than one fragmentation path is possible for a given molecule. Also, the fragments can suffer further fragmentations. In general, the most abundant fragment ion results from the fragmentations that form the most stable products (ion and neutral radical). The abundance of a fragment ion is affected by its stability. For this reason, the intensity of the response of a mass spectrometric detector can be very different for different molecular species, and the prediction of this intensity is difficult. As a result, the improvements in the sensitivity in EI + −type mass spectrometry (in GC/MS using EI+ ionization) are not usually sought (but not impossible) through derivatization.

Derivatization for enhancing sensitivity is, however, frequently applied in NCI-MS. In this technique, the electrons interact with the molecules of the CI gas which is lowering their energy but without forming ions. The ionization of analyte molecules takes place by interaction with the low-energy electrons or with already formed negative ions by electron capture, dissociative electron capture, ion pair formation, or ion molecule reaction. The ionization process with the formation of negative ions is efficient only for molecules with positive electron affinities. For this reason, the sensitivity in NCI-MS is highly dependent on the electron affinity of the analyte, similarly to the sensitivity in ECD. For enhancing the electron affinity, the derivatization with reagents containing, for example, fluorinated moieties (indicated in Figures 4, 5, 6) is practiced. The sensitivity of the analytical methods where such derivatization is applicable can have very good sensitivity. For example, derivatization with heptafluorobutyric anhydride of aromatic amines that are present at low trace level in cigarette smoke leads to limit of detection (LOD) values as low as 0.05 ng/cig. for compounds such as 4-aminobiphenyl [ 10, 11].

The fragmentation pattern generated by EI+ ionization mode that generates a specific mass spectrum of a molecule is very likely the most utilized technique for the identification of the molecular species. For this identification, large libraries of mass spectra are available, and computer algorithms are used for automatic searches. The identification of compounds using mass spectroscopy is not a simple process even with the capabilities offered by the electronic searches in the mass spectral libraries. This is particularly true for analysis of complex mixtures or when the analyzed compound is present in traces. Some compounds do not have a very characteristic mass spectrum, or during the chromatographic process, the separation is not achieved, and it is difficult to make an identification due to the spectra overlapping. Also, numerous compounds may have a mass spectrum that matches more than one compound (with a good quality fit). In such cases, a derivatization with the purpose of obtaining a compound that forms more informative fragments in the mass spectrum can be very useful.

The fragments from derivatized compounds can be used for the identification of unknown compounds using library searches and even when the mass spectrum is not available in the libraries. As an example, the derivatization by silylation allowed the identification of a new pentacyclic triterpenoid present in several bioactive botanicals [ 12]. An unidentified compound with MW = 456.7 was detected by LC/MS/MS in a rosemary extract. The structure of the compound was elucidated after silylation of the plant material based on the comparison of mass spectrum of the unidentified compound with that of silylated betulinic acid. The new compound was identified as (3β)-3-hydroxy-lupa-18,20(29)-dien-28-oic acid (or betul-18-en-oic acid). The mass spectra of the two acids are shown in Figure 7.

The two mass units difference between different fragments from the mass spectra of the two compounds allowed the identification of the new compound structure. Neither free betulinic acid nor betul-18-en-oic acid are volatile, such that the use of GC/MS for identification was possible only after derivatization.

Another special procedure that may be utilized for compound identification based on mass spectra is the use of two parallel derivatizations, one of them being done with an isotope-labeled reagent. Common labeling isotopes are ²H (deuterium, d), ¹³C, ¹⁵N, etc. One such isotopic labeling can be done, for example, using silylation with d ₁₈-N,O-bis(trimethylsilyl)-trifluoroacetamide (d ₁₈-BSTFA). Derivatization of an aliquot of sample with regular BSTFA and another with d ₁₈-BSTFA provides a pairing chromatogram with peaks at retention times that have only small differences from the first but with spectra differing by a number of units. The comparison of the spectra for corresponding peaks (based on retention time) of a given compound allows the calculation of the number of silyl groups attached to that compound. In addition, the fragmentation in the spectra can be better interpreted allowing easier compound identification.

Derivatization in GC/MS analysis may have multiple other utilizations and benefits. For example, quantitative analysis frequently utilizes isotopically labeled internal standards. In an analysis with multiple analytes, addition of an isotopically labeled internal standard for each analyte may become a complex process. When a derivatization is involved in the analysis, this can be done with a non-labeled reagent for the analytes in the sample, while the internal standards are obtained by derivatization of standards with the same reagent but isotopically labeled. Such technique has been proven to be very successful, for example, in the analysis of multiple amino acids (but using an LC/MS/MS procedure [ 13]).

6. General comments regarding the main types of chemical reactions used in derivatization

Derivatizations as chemical reactions can be classified as follows: (1) reactions with formation of alkyl or aryl derivatives, (2) silylation reactions, (3) reactions with formation of acyl derivatives, (4) reactions of addition to carbon-hetero multiple bonds, (5) reactions with formation of cyclic compounds, and (6) other reactions specific to a certain analysis. The selection of the derivatization reaction is typically done based on the desired property to be brought to the analyte and its possible reactivity. For this reason, the reagent is selected to have moieties that add the desired property to the analyte and also to have the capability to react with the specific functional group of the analyte. The matrix of the sample also has a role in the choice of a specific derivatization procedure. Initial matrix of the sample is not always suitable for derivatization, and in some cases preliminary sample preparation is necessary to change this matrix. The change can be as simple as drying the initial sample but can also be rather complex [ 14]. Table 1 gives a simplified view of preferences for the choice of a derivatization reagent for compounds containing active hydrogens [ 14].

Besides functionalities with active hydrogens, other functionalities can also be derivatized. Compounds containing carbonyls can be derivatized, for example, using condensation reactions. Some analytes may contain multiple functional groups such as the amino acids. Specific derivatization reactions can be selected for such cases.

7. Reactions with formation of alkyl or aryl derivatives

The formation of alkyl or aryl derivatives is applied to replace the active hydrogens from an analyte with an alkyl (R) or aryl (Ar) group. The replacement can be done in functionalities such as OH, COOH, SH, NH, or CONH. For example, the derivatization with short-chain alkyl bromides or iodides has numerous analytical applications for compounds such as steroids, amino acids, catecholamines, sulfonamides, phenols, barbiturates, organic acids, and mono- and oligosaccharides. A large number of reagents R-X are known, and in a simplified approach, it can be considered that R is carrying a specific property and X a specific reactivity, although the reactivity of a reagent is influenced by both R and X components of the molecule. The type of moiety R and that of reactive group X are guiding the selection process of selecting a reagent for a specific derivatization.

In most alkylation reactions, the analyte acts as a nucleophile (Y:, Y:H, Y:-) reacting in a substitution (SN) with the alkylating reagent R-X, which contains a leaving group X and an alkyl group R:

Various reagents and conditions were utilized in the derivatizations for analytical purposes. As reagents R-X for alkylations, one of the most commonly used are the alkyl halides, especially alkyl iodides and alkyl bromides. Because some of the derivatizations can be slow and inefficient depending on the analyte and on the reagent, the reaction rate becomes an important parameter for the analytical applicability. The reaction with an alkyl halide for the preparation of methyl or ethyl substituents, for example, is frequently performed either with a specific methylation reagent, in the presence of a catalyst, or in some instances using a particular solvent. The enhancement of the alkylation efficiency can be achieved using several other procedures. For example, for the analytical alkylation of carboxylic acids, specific cryptands such as crown ethers can be used to solvate the alkali metal portion of an organic acid salts, allowing the anion to be freer and increasing the rate of nucleophilic substitution. One other approach for enhancing the alkylation efficiency is the use of phase transfer alkylation. This approach is based on the formation of a compound easily extractable in an organic phase and on the displacement of the equilibrium in the direction of the formation of the desired product.

One different way of enhancing the alkylation efficiency is the use of different alkylating reagents besides short-chain alkyl bromides or iodides. One example of a halide that is particularly reactive is pentafluorobenzyl bromide. This reagent can be used for the derivatization of a variety of compounds containing active hydrogens. Another reactive halide is 2-bromoacetophenone (phenacyl bromide). This reagent is used mainly for the alkylation of compounds containing more acidic hydrogens such as carboxylic acids. Another example of methylation using a special reagent R-X is applied on carbohydrates [ 15]. This methylation uses methylsulfinylmethanide anion. The reagent is prepared from dry DMSO and NaH or KH in a reaction as follows:

A polyol or a monosaccharide dissolved in DMSO is easily methylated with methylsulfinyl-methanide anion.

Other alkylating reagents are known (different X in R-X), also reacting in a nucleophilic substitution. For example, dimethyl sulfate can be used for alkylations. Alkylfluoromethyl-sulfonates are even more reactive than sulfates, and the reaction may take place with the active hydrogen even from alcohols or amines as follows:

Even tertiary amines, such as pyridine, also react with this type of reagent forming quaternary ammonium salts. The alkylation with alkylfluorosulfonates can be catalyzed as other alkylation reactions for increasing the reaction rate. A catalyst that can be used in this reaction is Hg(CN) ₂.

Diazomethane is another common alkylating (methylating) reagent. The alkylation using diazomethane is assumed to take place as follows:

Diazomethane is a gaseous unstable substance, which cannot be stored for long periods of time. It is usually prepared in small quantities and used immediately with or without an intermediate step of dissolution in ether. The preparation can be done from different N-nitroso-N-alkyl compounds in a reaction with a base. A common preparation uses N-nitroso-N-alkyl-p-toluenesulfonamide (Diazald). Methylation with diazomethane may require addition of a Lewis acid catalyst such as BF ₃. The methylation of partly acetylated sugars and amino sugars using diazomethane and BF ₃ in ether leads to the methylation of the free OH groups without the migration or substitution of the existent acyl groups.

A common alkylation of acidic analytes such as carboxylic acids, phenols, and thiols is performed using another type of alkylating reagent, namely, N,N-dimethylformamide dialkyl acetals. N,N-Dimethylformamide dimethyl acetal (Methyl-8 ®) is commonly used for methylations. For a compound containing a COOH group, the reaction with this reagent takes place as follows:

The compounds with acidic hydrogens can also be alkylated (methylated) using trimethyl orthoacetate, alkyl-p-tolyltriazenes (R─NH─N═N─C ₆H ₄─CH ₃), and O-alkyl isoureas are also used for the formation of analytes containing acidic hydrogens, imino esters, etc.

Alcohols can also act as alkylating reagents in particular when the analyte contains a more acidic hydrogen. Catalyst such as HCl, BF ₃, CF ₃ COOH or a cation exchange resin in H⁺ form is also frequently added to facilitate the reaction. The addition of HCl can be made as a water solution or as gaseous HCl that does not bring additional water to the reaction medium. The formation of alkyl or aryl derivatives of acids is a particularly important reaction known as esterification. Derivatization by esterification has been used with acids as the analyte and the alcohol as the reagent and also with the alcohol as the analyte and the acid the reagent. The esterification can be viewed either as the acid alkylation or as the acylation of the alcohol (see also the esterification mechanism). This reaction is typically catalyzed by strong acids and can be written as follows:

The mechanism of ester formation can be summarized by the following series of reactions:

The esterification efficiency can be improved by removing the water formed in this reaction. This can be done using a chemical reagent or distillation when the compounds of interest boil above 100°C. Among the materials able to eliminate water are desiccants such as anhydrous MgSO ₄, molecular sieves, or substances that react with water such as CaC ₂, (CH ₃) ₂C(OCH ₃) ₂ (2,2-dimethoxypropane), and even an appropriately chosen acid anhydride that reacts faster with water than with the reacting alcohol. The derivatization also may be performed in the presence of SOCl ₂ (thionyl chloride), which reacts with the water assisting in its removal, and when present in excess, may react with the alcohols forming alkyl chlorides or with the acids forming acyl chlorides. Chloride is a better leaving group in a nucleophilic alkylation reaction, and the efficiency of alkylation increases. Acids also can be esterified using a mixture of an alcohol and an acyl halide.

One procedure for the formation of esters with less active organic acids applies the addition of dicyclohexylcarbodiimide (DCCI) in the derivatization process, to facilitate esterification. The reaction can be performed by adding to the acids that need to be analyzed the appropriate alcohol and DCCI usually in a solvent such as pyridine. Dicyclohexylurea, which is formed in the reaction, is not soluble in pyridine and can be separated. Besides DCCI, other carbodiimides can be used in the reaction of acids and alcohols. Among these are carbonyldiimidazole (CDI), 6-chloro-1-p-chlorobenzensulfonyloxybenzotriazole (CCBBT), 1-(3-dimethylaminopropyl)-3-ethylcarbodiimide (EDAC), etc. Also, 2-chloro-1-methylpyridinium iodide, 2,4,6-triisopropylbenzenesulfonyl chloride, trialkyloxonium fluoroborate, etc. can be used to facilitate esterification.

Transesterification is another technique applicable for obtaining certain alkyl derivatives of acids (or acyl derivatives of alcohols). The reaction can be written as follows:

Transesterification can be catalyzed by acids (or Lewis acids) such as HCl, BF ₃, and H ₂SO ₄ or by bases such as CH ₃OK, CH ₃ONa, or C ₄H ₉ONa. The basic catalysts are commonly used for the methanolysis of triglycerides, followed by the analysis of the fatty acid methyl esters using GC or GC/MS [ 16].

A special alkylation can be achieved online during the heating in the injection port of a gas chromatograph using tertraalkylammonium hydroxides or alkylarylammonium hydroxides. Tetramethylammonium hydroxide (TMAH) is the most common reagents of this type. The reaction takes place as follows (Δ indicates heating):

Numerous other reactive compounds may be used for replacing active hydrogens in specific compounds. For example, epoxides, aziridines, and episulfides react easily with compounds with active hydrogens. Formation of a second group containing an active hydrogen may preclude the use of such reagents for analytical purposes.

Besides the desired derivatives, certain unexpected compounds that can be considered artifacts for the particular analysis can also be formed in alkylation reactions. The artifacts may be formed from unexpected interactions of the reagent with the analyte or may be a result of undesired effects of the catalysts or medium used for derivatization. In some cases, the control of the alkylation process may be difficult. Longer or shorter reaction times or intervals between derivatization and analysis may lead to errors, even when an internal standard is used for quantitation.

One common case of artifact formation occurs during the reaction with compounds containing O-acyl or N-acyl groups, such as previously acylated carbohydrates, glycolipids, or glycoproteins, in particular when the reaction is done with short-chain alkyl bromides or iodides. When the OH groups of different sugars or NH ₂ groups of amino sugars were already protected with acyl groups, it was noted that, depending on the catalyst and the chosen medium, these acyl groups can be replaced by alkyl groups, or they may migrate from one position (such as C1) to other positions.

Oxidation is another common side reaction when using Ag ₂O as a catalyst. The oxidation effect of Ag ₂O can be seen on free sugars as well as when attempting to permethylate peptides. Sulfhydryl groups are particularly sensitive to oxidation with Ag ₂ O as a catalyst. The use of methylsulfinyl carbanion as a methylating reagent may also produce undesired side reactions with certain esters generating methylsulfinylketones. Also, strong alkylating reagents may produce undesired artifacts by unexpected alkylations.

The derivatization with the purpose of obtaining aryl derivatives is similar in many respects to the alkylation reaction. The reaction takes place with compounds containing active hydrogens. Simple aryl halides are generally resistant to be attacked by nucleophiles and do not react similar to alkyl halides. This low reactivity can be significantly increased by changes in the structure of aryl halide or in the reaction conditions. The nucleophilic displacement can become very rapid when the aryl halide is substituted with electron attracting groups such as NO ₂.

8. Silylation reactions

Silylation is the chemical reaction of replacing a reactive hydrogen atom in OH, COOH, SH, NH, CONH, POH, SOH, or enolisable carbonyl with a silyl group, most frequently with trimethylsilyl (TMS). A large number of analytical methods involve silylation applied to alcohols including carbohydrates [ 17], phenols [ 18], amines, sterols [ 19], etc. The purpose of silylation in chromatography is mainly to reduce the polarity of the analyte, increase its stability, and improve the GC behavior. The differences in the mass spectra of the silylated compounds as compared to the initial analyte may also be an advantage for detectability. However, the mass spectra of many silylated compounds may not be available in common mass spectral libraries. Also, the silylated compounds plus the commonly present excess of silylating reagent may deteriorate some types of stationary phases such as that of Carbowax (polyethylene glycol)-type columns, and for this reason, their separation cannot be done on such columns.

Silylation can be performed on specific analytes or directly on complex samples such as a plant material (see, e.g., [ 12]). The silylating agent and the solvent can play the double role of extractant and silylating reagent. Many publications describe the use of silylation reactions for analytical purposes (e.g., [ 1, 5, 20]). The reaction of an analyte Y: H with the formation of a TMS derivative can be written as follows:

The molecular weight for TMS is 73.047 calculated considering in the elemental composition of only the masses of the most abundant isotope. Numerous reagents have been synthesized to be used in silylations. Various aprotic solvents can be used as medium for silylation. The analysis can be focused on one analyte or on a mixture of analytes. The main factors contributing to the increase of the efficiency and the rate of the silylation reaction are the silyl donor ability of the reagent and the ease of silylation of different functional groups in the analyte. The solvent (or mixture of solvents) used as a medium and the compounds present or added in the silylation medium may also play a role for silylation efficiency. The reagent excess is sometimes important for displacing the equilibrium in the desired direction, and usually an excess up to ten times larger than stoichiometrically needed is used for silylation. Temperature also increases reaction rate, as expected, and heating of the sample with the reagents at temperatures around 70°C for 15 to 30 min is common. Some reagents used for trimethylsilylation are shown in Figure 8 [ 14].

The approximate order of the increasing silyl donor ability for the reagents shown in Figure 8 is HMDS < TMCS < MSA < TMSA < TMSDEA < TMSDMA < MSTFA < BSA < BSTFA < TMSI. This order may be different on particular substrates where other reagents or reagent mixtures may be more reactive.

Silylation reagents can be used pure or in mixtures of two or even three reagents. The reagent mixtures may provide a more efficient silylation for specific compounds. For example, silylation of 3,4-dimethoxyphenylethylamine with BSA leads to the substitution of only one active hydrogen in the NH ₂ group, while the silylation with BSA in the presence of 5% TMCS produces silylation of both hydrogens in the NH ₂ [ 21]. A common silylating mixture is BSTFA with 1% TMCS.

One of the determining factors regarding the silylation efficiency is the nature of the molecule Y:H that is being silylated (the analyte) and plays a crucial role in the choice of the derivatization conditions. It was noticed experimentally that the decreasing ease of silylation follows approximately the order shown in Table 2.

In general, the silylation of OH and COOH groups takes place with better results than that of NH ₂, CONH, or NH groups. Excellent results are obtained, for example, for the analysis of phenols after silylation [ 19]. A chromatogram of a solution containing a mixture of phenols at concentrations between 2.0 and 2.5 μg/mL in DMF, derivatized with BSTFA, separated on a BPX-5 chromatographic column (SGE Anal. Sci.), followed by MS analysis in single-ion monitoring (SIM) mode is shown in Figure 9. Details regarding the analyzed phenols are given in Table 3.

Besides organic active hydrogens, several inorganic compounds with active hydrogens can also react with silylating reagents. Among these are H ₂O, H ₂O ₂, and strong inorganic acids. Also, some salts of the acids may be silylated. The reaction of silylating reagents with water imposes that water should be at the low level in the matrix or the solution of the analytes. The reaction with water takes place as follows:

In many solvents used as medium for derivatization, the trimethylsilanol formed in the reaction with water is separated as a distinct layer of solvent. The formation of two layers impedes a proper sampling of the derivatized material in the GC/MS instrument. In addition to that, the presence of an excess of water suppresses the derivatization of other compounds. The silylation is not recommended on samples with a water content higher than about 10%.

The silylation reaction is commonly performed in a solvent that does not have active hydrogens. The most commonly used solvents as a medium for silylation are dimethylformamide (DMF), pyridine, and acetonitrile. The main role of the solvent is to dissolve the analyte and the reagents. The by-product HX of silylation shown in reaction (17) can be an acid, a base, or a neutral compound. As examples, for TMCS the by-product is HCl, for HMDS the by-product is NH ₃, for BSTFA the by-product is N-TMS-trifluoroacetamide, and for TMSI the by-product is imidazole. When the silylation reagent generates an acid as a by-product of the reaction, this may interfere with the silylation. For this reason, silylation can be promoted by any acid acceptor used as solvent or present in the solvent. Among such solvents are pyridine, triethylamine, and to a lower extent DMF. They can be used as both solvents and acid acceptors. Mixtures of solvents are commonly used for both enhancing solubility and promoting silylation. For example, formamide in the presence of pyridine may react with an acidic by-product generating CO and an ammonium salt. The addition of basic compounds to the silylation reaction may also influence the efficiency of the silylation. Also, some compounds may act as catalysts for silylation.

Although the TMS derivatives are by far the most commonly used in the derivatization for analytical purposes, other substituents in the silyl group can be used as reagents. Several such groups are indicated in Figure 10. The groups can be present in a variety of reagents connected to leaving groups "X-" such as Cl-, imidazolyl, F ₃C-(CO)-N(CH ₃)-, etc. For example, a common reagent containing tert-butyldimethylsilyl group is N-methyl-N-( tert-butyldimethylsilyl)-trifluoroacetamide (MTBSTFA), which has the following structure:

The use of different groups than TMS may serve different purposes. For example, a fluorinated or brominated group may enhance significantly the detection sensitivity when using ECD or NCI-MS. Also, the stability toward hydrolysis of compounds silylated with different groups than TMS may be higher, and such silylation can be advantageous. This is, for example, the case of tert-butyldimethylsilyl group that is typically more stable to hydrolysis than trimethylsilyl.

As an example, silylation of amino acids with MTBSTFA is commonly used [ 22, 23], and it is preferred to the silylation generating TMS derivatives. The chromatogram of a set of amino acid standards with the concentration of 0.05 μmol/mL derivatized with MTBSTFA and separated on a DB-5MS chromatographic column (from Agilent) followed by MS analysis is shown in Figure 11. Details regarding the analyzed amino acids are given in Table 4.

In most situations, silylation generates only the desired derivatives. However, there are cases when the expected silylated compound is not formed, and either the silylation is not complete, or some compounds such as aldehydes, ketones, or esters with no obvious active hydrogen generate silylated compounds. Incomplete silylation is usually the result of inappropriate reaction conditions. However, when compounds with multiple functionalities are silylated, it is possible to generate a variety of derivatized compounds, regardless of the intention to obtain fully silylated or partly silylated compounds.

In some cases, artifacts are formed due to the modification of the analyte under the influence of the reagents during derivatization. For example, when the silylation is done in basic or acidic conditions, the analytes that are sensitive to acidic or basic media may suffer unexpected transformations. The most frequent artifacts with compounds not containing obvious active hydrogens occur with aldehydes. Some aldehydes are able to undergo two types of chemical reactions with formation of OH groups, namely, enolization and acetal formation in the presence of water. The OH groups formed in this manner react with different silylating reagents and give the corresponding silylated products. Although the enolization or the acetal formation is negligible for the initial aldehyde, the reactions may be significantly displaced toward the formation of the silylated compounds of the enol or of the acetal. Artifacts can also be generated when the reaction is allowed to continue for an extended period of time. Other uncommon reactions with a specific silylation reagent and analyte may occur. An example of an uncommon reaction is the ring opening of flavanones.

9. Acylation reactions

The formation of acyl derivatives is applied for replacing the active hydrogens from an analyte in functionalities such as OH, SH, NH [ 11, 24], CONH, etc. The acylation is also used for reducing polarity and improving the behavior of the analytes in the chromatographic column. Acylation may confer a better volatility of the analytes, although not as marked as for silylation or methylation. Only the derivatization with acetyl groups or with fluorinated acyl groups (not heavier than heptafluorobutyryl) improves volatility, while other heavier acyl groups are not suitable for this purpose. Acetylation, for example, can be used for compounds such as monosaccharides and amino acids to allow GC analysis. The detectability improvement on the other hand is a very common purpose for acylation. Acylation with fluorinated compounds is frequently used for enhancing detectability in GC with ECD or NCI-MS detection. Other uses of acylation include the enhancement of separation of chiral compounds, etc.

Most acylation reactions are nucleophilic substitutions where the analyte is a nucleophile (Y:, Y:H, Y:-) reacting with the acylating reagent RCO-X that contains a leaving group X and an acyl group RCO: as shown in the following reaction:

Some common acyl groups present in acylation reagents are indicated in Table 5.

As shown in Table 5, the acyl groups in the reagent can be attached to various "X" groups. One such group is OH and among the acylating reagents are some free acids. When nucleophile is an alcohol, the reaction is known as esterification and has been discussed in Section 7. The acylation with acids can be applied besides alcohols to certain thiols, phenols, amines, etc. and can be written as follows:

The reaction can be displaced toward the formation of the acyl derivatives by eliminating the water using compounds such as anhydrous MgSO ₄, molecular sieve, or substances that react with water such as CaC ₂, or (CH ₃) ₂C(OCH ₃) ₂. Dicyclohexylcarbodiimide (DCCI) also is used for modifying the yield of the desired product. The reaction with reagents containing a carboxylic acid reactive group also can be done in the presence of 2,4,6-trichlorobenzoyl chloride or with various sulfonyl chlorides such as 2,4,6-triisopropyl-benzenesulfonyl chloride or 2,4,6-trimethyl-benzenesulfonyl chloride. The reaction of amines with acids can be displaced toward the formation of the amides using a peptide coupling reagent such as benzotriazol-1-yl-oxy-tris(dimethyl-amino)-phosphonium hexafluorophosphate (BOP), diethyl cyanophosphonate, O-benzotriazol-1-yl-N,N,N′,N′-bis(tetramethylene)uronium hexafluorophosphate, 2,2′-dipyridyl disulfide + triphenylphosphine, 1-(3-dimethylaminopropyl)-3-ethylcarbodiimide (EDAC), etc.

Common acylating reagents are acyl halides such as chlorides or bromides, which are reactive compounds suitable for acylation. The reaction of an acyl chloride with an amine, for example, takes place as follows:

Since the reactivity of amides is lower than that of amines, the second hydrogen in the amine is more difficult to replace. Also, steric hindrance may negatively influence the reaction. The generation of a strong acid such as HCl is a disadvantage in the reaction with acyl halides, and usually the acid should be removed either by adding basic compounds such as Na ₂CO ₃ or MgCO ₃ or using pyridine as the reaction medium. The high reactivity of acyl halides is used for the acylation of compounds with less reactive hydrogens. Certain carbonyl cyanides react similarly to acyl chlorides.

The disadvantage of generating a strong inorganic acid in the acylation with acyl halides also can be avoided by having, instead of the acyl halide, an anhydride. The reaction of Y: H with an anhydride takes place as follows:

The acid resulting together with the acylated compound is not a strong acid such as HCl. The anhydrides of trifluoroacetic acid (TFA), pentafluoropropionic anhydride (PFPA), and heptafluorobutyric (HFBA) acids are commonly used for derivatization of alcohols, phenols, amines, etc., with the purpose of enhancing detectability (by ECD or NCI-MS) and also for improving the chromatographic behavior (higher volatility, better thermal stability, better separation). The volatility of fluorinated compounds allows the GC applications. The reactivity of the perfluorinated anhydrides increases in the order HFBA < PFPA < TFA. However, the differences are not significant. Once formed, the heptafluorobutyrates are more stable to hydrolysis than the trifluoroacetates. An inert solvent such as CH ₂Cl ₂, ether, ethyl acetate, acetone, tetrahydrofuran or in CH ₃CN, etc. can be used as a medium for the reaction with perfluoroanhydrides. For the neutralization of the acids formed during derivatization, the basic compounds such as triethylamine, pyridine, or even solid NaHCO ₃ can be utilized.

In order to avoid the formation of water or of a strong acid in the acylation reaction, certain amides such as N-methyl-bis(trifluoroacetamide), bis(trifluoroacetamide), or 2,2,2-trifluoro-N-methyl-N-(2,2,2-trifluoroacetyl)acetamide (MBTFA) can be used as reagents. Acylation of amines takes place at room temperature. Solvents such as CH ₃ CN, pyridine, DMSO, or THF can be used as a reaction medium:

One other procedure successfully applied to obtain acyl derivatives is the use of acyl imidazoles as reagents. This class of compounds reacts with analytes containing alcohol, primary and secondary amino groups, or thiols. The reaction generates as a by-product imidazole:

Succinimidyl esters also can be used for acylation purposes. Amines and the amino group in amino acids also can be acylated using urethane-protected α-amino acid-N-carboxyanhydrides or oxycarbonyl-amino acid-N-carboxyanhydrides. Alkylketenes and their dimers may be used for acylation.

A special type of acylation is that using chloroformates. Carbonic acid, O═C(OH) ₂, can form amides, esters, halides, etc., due to the presence of two OH groups bonded to the CO group. Carbonic acid ester halides, also called chloroformates or chloroformate esters, with the formula R─O─C(═O)─X, where R is an alkyl or aryl group and X is F, Cl, Br, or I, can react with various compounds containing active hydrogens, such as acids [ 25], amines, alcohols, thiols, and amino acids. Amino acids, for example, in the presence of an alcohol in water form carbamate esters (urethanes) reacting as follows [ 26]:

The formation in reaction (25) of the alcohol R a-OH may lead to traces of a resulting compound with both substituted radicals being R a. For this reason it is typically recommended to perform the reaction in the presence of an alcohol having the same radical as the chloroformate reagent (R a = R b). Chloroformates containing in the alkyl or aryl group halogen substituents are particularly reactive. Even tertiary amines can react with specific chloroformates, such as pentafluorobenzoyl chloroformate or with trichloroethyl chloroformate, by displacing an alkyl group connected to the nitrogen atom and forming the carbamate ester.

Similar in many respects to that of acyl derivatives R-CO-X are the reactions of sulfonyl derivatives R-SO ₂-X. Sulfonyl halides are in general less reactive than halides of carboxylic acids. The reaction of a sulfonyl derivative may take place with alcohols, phenols, amines, etc. The reactivity toward the sulfonyl sulfur is RNH ₂ > CH ₃COOR > H ₂ O > ROH.

High reactivity toward active hydrogens in alcohols, amines, etc. can also be achieved using reagents with other functionalities. These functionalities include isocyanates, isothiocyanates, carbonyl azides, etc. These reactions can be seen as a replacement of an active hydrogen with a CO-R group or CS-R group as it occurs in other acylations.

10. Other derivatization reactions

A variety of other derivatization reactions are reported in the literature (see, e.g., [ 1]) and used for GC and GC/MS analyses. Among these are the addition to hetero multiple bonds in functional groups such as C═O, C═S, C═N, or C☰N. Many such reactions are additions to multiple bonds. Such reactions are, for example, the additions to the C═O groups in aldehydes and ketones. Reagents containing active hydrogens in groups such as NH ₂, OH, H ₂N-NH-, etc. can react, for example, with aldehydes and ketones. Alcohols, for example, form hemiacetals or acetals with aldehydes and ketals with ketones, and although most of such compounds are not stable enough to be suitable for derivatization, cyclic acetals and ketals may be stable and used for analytical purposes. A common reaction of carbonyl compounds is with amines. The initial addition reaction usually continues with water elimination forming a substituted imine or a Schiff base. Similar to the reaction of amines is the reaction with substituted hydroxylamines or hydrazines. A typical reaction of derivatization of carbonyl compounds is that using dinitrophenylhydrazine (DNPH). The derivatized compound can be analyzed either by LC [ 27] or by GC/MS [ 28]. The reaction takes place as follows:

The groups R a and R b can be H or alkyl or various other substituents.

Another reagent that can be used for ketone derivatization is N-aminopiperidine in the presence of catalytic amounts of acetic acid. The resulting substituted hydrazone can be used in GC analysis:

β-Diketones may react differently with hydrazines generating pyrazole derivatives as shown below:

Several other classes of compounds similar to hydrazines react with the carbonyl compounds. Among these are hydrazones (NH ₂─N═CR ₂), hydrazides (NH ₂NH-COR), and semicarbazide (NH ₂NH-CONH ₂). Hydroxylamines also react with carbonyl compounds forming oximes. Hydroxylamine itself, hydroxylamine hydrochloride (STOX® reagent), or derivatives such as H ₂N-OSO ₃ H in a solvent like pyridine can be used in this reaction:

When the reaction is performed with hydroxylamine, the generated oxime contains an active hydrogen. This can be further derivatized, for example, by silylation in a reaction with a common silylation reagent.

For derivatization purposes other reagents can be used, such as substituted hydroxylamines like methoxyamine hydrochloride NH ₂OCH ₃ *HCl (MOX® reagent) and O-(pentafluorobenzyl)-hydroxylamine hydrochloride (FLOROX® reagent). The reaction of a ketone or aldehyde with FLOROX is shown below:

The oximes existing in sin- and anti- forms can produce double peaks in the GC chromatographic separations. To avoid this effect, oximes can be converted into nitriles when treated with acetic anhydride in the presence of CH ₃ COONa. This reaction was used in the derivatization of carbohydrates when a simultaneous acetylation takes place (Wohl degradation). The reaction can be written as follows:

The transformation of the oximes into nitriles generates one single compound from the two (syn- and anti-) isomers and can be used to simplify the chromatograms of sugars derivatized as oximes.

Alcohols, amines, and thiols also can react at other hetero multiple bonds leading to analytical applications. This addition may occur at the isocyanates (─N═C═O), ─C═O group in an amide, at a nitrile, at CS ₂, or at other groups. One example is the addition under special conditions of alcohols to dimethylformamide. The resulting acetals are very reactive and are used themselves as reagents, as shown previously for N,N-dimethylformamide dimethyl acetal (see reaction 12). Another example is the reaction of CS ₂ with alcohols in the presence of a base, leading to the formation of xanthates. Amines also react with CS ₂, and the formed isothiocyanate can be analyzed using GC analysis. The reaction takes place as follows:

Formation of new cycles from noncyclic compounds or replacement of old cycles with new ones that are more stable or have a desired property is also exploited in sample processing using derivatization. Epoxides, for example, can be formed in the reaction of a compound with a carbon-carbon double bond and a peroxy acid. Among the peroxy acids more frequently used for the formation of epoxides are peracetic, performic, perbenzoic, trifluoroperacetic, and 3,5-dinitroperoxybenzoic acids. However, in this reaction a mixture of enantiomers is formed, as shown below for a cis olefin:

The separation of the epoxides may be easier to achieve than that of olefins, and this type of derivatization has been utilized, for example, for better separation of various cis and trans unsaturated fatty esters.

Another reaction with formation of new cycles is that of amino acids with phenyl isothiocyanate leading to a thiohydantoin derivative:

This reaction has been successfully used for the analysis of amino acids in proteins [ 29, 30]. p-Bromophenyl isothiocyanate has been used in a similar reaction.

A variety of aromatic cycles can be formed in reactions involving bifunctional compounds. Addition reactions to hetero multiple bonds in bifunctional molecules frequently lead to cyclic compounds. For example, formaldehyde can react with tryptophan or tryptamine generating a β-carboline derivative as follows:

The new compound can be analyzed by GC, usually after further derivatization by silylation of the carboxyl group.

A typical reaction leading to pyrazoles is the reaction of hydrazines with diketones such as 2,4-pentandione (acetylacetone). For example, the reaction between hydrazine or methylhydrazine and acetylacetone takes place as follows:

The resulting compound can be analyzed using a GC separation.

Activated carbonyl groups such as those in hexafluoroacetone are known to react with difunctional compounds. The reaction may take place with an amino acid as follows:

Amino acids can react with an activated anhydride such as trifluoroacetic anhydride (TFAA):

The reaction takes place by heating the amino acids with an excess of TFAA. The reaction mixture is then dissolved in ethyl acetate and analyzed by GC.

Numerous other types of derivatization reactions were used for making the analytes suitable for GC and GC/MS analyses. These include formation of various cyclic types of compounds such as azines, siliconides, boronates, etc., that are thermally stable and do not have polar hydrogens such that GC or GC/MS analysis is possible. In addition to reagents that add specific moieties to the analytes, oxidation and reduction were sometimes used for the analyte modification (see, e.g., [ 4]).

11. Derivatization reactions involving solid-phase reagents or derivatization on a solid support

Solid-phase reagents are polymeric materials with specific groups that are reactive and can be transferred to the analyte molecule producing derivatization. For an analyte of the form Y: H, the reaction with a solid-phase reagent can be written as follows:

Solid-phase reagents must work analogously to the corresponding small-molecule reagents containing the group R (a tag). Reagents that are insoluble in certain solvents at high concentrations can often provide a high ratio of analyte/substrate in a polymeric microenvironment that yields a high kinetic rate for the heterogeneous reaction.

A variety of materials can be used as solid support, such as specifically bound reagents on a silica support (used, e.g., for online derivatization in HPLC analysis), ion exchange resins, as well as other supports [ 31]. One example of solid-phase support that can produce derivatization is trifluoroacetyl nylon 6,6. This solid-phase reagent can be obtained from poly(hexamethylene adipamide) (nylon 6,6) and trifluoroacetyl anhydride. This solid-phase reagent can be used in amine derivatization in a reaction as follows:

This derivatization of the amine is done by mixing the solid-phase reagent with a solution of amine solution in CH ₃ CN. Following derivatization, the solid-phase reagent is separated by centrifugation, and the solution is concentrated by evaporating part of the solvent and analyzed by GC (an amine internal standard must be used in this procedure). However, some such derivatizations require a long time of interaction between the solid-phase reagent and the analytes and found only limited applications.

(Another) alternative of derivatization of specific analytes is using the reaction between the reagent and the analyte both adsorbed on a solid support. This type of derivatization has been used, for example, in connection with a solid-phase microextraction (SPME) technique [ 32]. In this technique a reagent is initially adsorbed in the SPME fiber, followed by exposure to the analytes. The derivatized analytes are further desorbed in the injection port of the GC and analyzed using a detector such as MS. For example, formaldehyde from air can be analyzed using a polydimethylsiloxane (PDMS) fiber containing o-(2,3,4,5,6-pentafluorobenzyl)-hydroxylamine hydrochloride as a reagent. After exposing the fiber to the air sample contaminated with formaldehyde, the derivatization agent reacts with formaldehyde absorbed onto the coating forming an oxime. The oxime is thermally desorbed in a GC injector port and analyzed by GC with ECD [ 33].

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Derivatization Methods in GC and GC/MS | IntechOpen

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The first part of this chapter presents the main objectives for performing derivatization of a sample to be analyzed by gas chromatography (GC) or gas chromatography/mass spectrometry (GC/MS). The derivatization is typically done to change the analyte properties for a better separation and also for enhancing the method sensitivity.

# data[].attributes.body
1. gas chromatography & mass spectrometry
2. volatility & mass
3. False. GC-MS is used for detection of congenital metabolic diseases.
4. False. GC-MS has advantages of higher specificity compared to soft ionization techniques used in LC-MS.
5. mass spectrum or chromatogram

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1. Two techniques used are: separating the components of a sample using ________ and identifying the components at a molecular level ________.
2. GC-MS allows us to separate individual components according to ________ and ________.
3. True/False. GC-MS is also used in medicine as a test for detection of infectious diseases
4. True/False. One disadvantage of GC-MS is its low specificity for organic acids compared to other techniques.
5.  The final readout of the peak heights is called the _________.

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Before you start indulging on the facts, kindly play this.

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▶️ A video on Mass Spectrometry

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Mass spectrometry | Atomic structure and properties | AP Chemistry | Khan Academy

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👋 Hi there! Here is a simplified video about Gas Chromatography

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Gas chromatography | Chemical processes | MCAT | Khan Academy

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https://www.shsu.edu/%7Echm_tgc/sounds/pushmovies/gcms.html

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An Enhanced GC/MS Procedure for the Identification of Proteins in Paint Microsamples

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An Enhanced GC/MS Procedure for the Identification of Proteins in Paint Microsamples

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The chemical characterization of materials used in works of art is extremely useful for gaining a better knowledge of the artistic heritage and to guarantee its preservation. A derivatization GC/MS procedure for the identification of proteins in a microsample from painted works of art has been optimized. The amino acid fraction is derivatized using anhydrous dimethylformamide (DMF) as solvent instead of pyridine (Py), commonly used to facilitate the reaction. Although pyridine is often considered a silylation catalyst, there are many instances in which silylation reactions actually are slower in pyridine than other solvents. In addition, pyridine also may have other undesirable effects such as the promotion of secondary products and other chromatographic anomalies. Using DMF, the formation of artifacts is limited and the derivatization yield of hydrophilic amino acids such as proline and hydroxyproline has improved, thus making the identification of organic paint media more straightforward. The method has been validated and successfully applied to identify the binder of the sample taken from the pictorial cycle of the 12th century monastery of Santa Maria delle Cerrate (Lecce, Italy), thus highlighting the use of eggs as a binding medium.

1. Introduction

Proteinaceous paint media, especially those based on egg and milk, have been used over the centuries by painters, due to excellent optical features and stability of the paint films obtained [ 1, 2]. The chemical characterization of materials used is extremely useful for surveying historical treatments and for gaining a better knowledge of the artistic heritage. This study entails investigating the original constituent materials and those become an integral part of the work of art itself as an effect of aging and restorations. Assessing the state of conservation of a painting is fundamental when choosing a conservative strategy based on both prevention and intervention. Developing analytical techniques for understanding the chemical composition of painting materials and for studying the degradation process is thus of great importance.

In the conservation of paintings, a special care and attention to organic materials is necessary, due to their relatively increased tendency to undergo degradation, transformation, and oxidation process [ 2]. In fact, the paint layers may exhibit cracking, darkening, yellowing, and loss of stability and cohesion. Milk or casein, animal glue, egg, drying oils (walnut, linseed, and poppy seed oils), plant resins (e.g., sandarac, mastic, and colophony), animal resins, and waxes are the most common organic materials historically used in the Mediterranean area and in Europe. The chemical composition and physical properties of these organic materials (mixtures of organic species consisting of proteins, triglycerides, terpene compounds, sterols, esters, alcohols, hydrocarbons, free acid, etc.) are considerably influenced by environment and aging [ 1, 2]. A wide range of natural and synthetic organic materials was used for consolidation and restoration [ 1]. Organic materials were used as binders to disperse pigments and to apply them on all the supports, thereby generating an adherent and elastic film. Proteinaceous materials were thus employed in the tempera technique, drying oil in the oil technique, and beeswax in encaustic painting [ 1, 2]. The precise study of organic materials in cultural heritage works was investigated on painting cross sections since the early 1970s using staining methods with organic dyes such as naphthol blue black [ 2] for the specific detection of proteins, allowing an in situ microcharacterization of egg-based or collagen-based materials. After that, spectroscopic techniques were employed for characterization of proteinaceous binders, as well as calorimetric analysis (DSC and TGA) for investigated proteinaceous materials and interference with inorganic materials [ 3]. Methods based on fluorescence detection HPLC-FD and HPLC-DAD UV diode array detection were employed to study amino acid composition of binders [ 4], but GC/MS is above all the most common method used for the characterization of organic species in pictorial samples and their behavior with aging.

Different GC/MS analytical procedures for the characterization of proteins [ 2, 5- 8], lipids [ 9- 12], plant resins [ 13- 17], animal resins [ 18], and waxes [ 19- 21] are described in the literature.

Generally proteins, following ammonia extraction and acid-catalysed hydrolysis, are identified by quantifying the free amino acids by GC/MS after derivatization [ 22- 24]. In this paper, the optimization of derivatization GC/MS procedure for the identification of proteins in the paint microsample is presented. Silyl derivatives are probably the most widely used derivatives for GC applications [ 25]. However, silylation involves reaction with active hydrogen atoms; for these reasons, all solvents containing or capable of activating these groups (e.g., alcohols, acids, primary and secondary amines, mercaptans, and primary and enolizable ketones) should be avoided. Often, an excess of the silylation reagent itself can act as the solvent, eliminating the need for additional components in the analytical scheme. Hexane, ether, benzene, or toluene is an excellent solvent for the reaction products, but they do not accelerate the rate of reaction. Among polar solvents, pyridine (Py) has been generally used in the identification of organic binders in paints [ 22, 26, 27]. However, there are many examples in which silylation reactions are actually slow in pyridine, for instance, with hydrophilic amino acids such as proline and hydroxyproline [ 28]. In addition, pyridine also may have other undesirable effects such as the promotion of secondary products and other chromatographic anomalies [ 29].

In the present paper, the amino acid fraction obtained after extraction and hydrolysis of different proteinaceous binding media [ 22] has been derivatized using anhydrous dimethylformamide (DMF) as solvent instead of pyridine. DMF has been used especially for large molecules [ 26, 28]; in the present case, it is shown to be effective for small molecules such as the amino acids as well. Moreover, by using DMF, we can limit the formation of artifacts [ 29, 30] and improve the derivatization yield for hydrophilic amino acids such as proline and hydroxyproline.

Reference samples produced by a local restorer were employed to test and validate the analytical procedure. The percentage contents of amino acids, obtained by the proposed procedures, were submitted to correlation analysis with those obtained from egg protein, animal glue, and milk hydrolysis in order to achieve the identification of the proteinaceous binders present in the sample. Finally, the results relevant to the sample from the wall paintings from Santa Maria delle Cerrate (Lecce, Apulia, Italy), a monastery of the first half of the 12th century, are presented.

2. Experimental

2.1. Reagents

All the solvents were HPLC grade. Hexadecane (I.S.1), triethylamine and N-tert-butyldimethylsilyl- N-methyltrifluoroacetamide (MTBSTFA) were purchased from Fluka (USA), Merk (Germany), and Sigma-Aldrich (Italy), respectively. All reagents and chemicals were used without any further purification. Ultrapure water (Integra UV, Diessechem, Italy) was used all through. Standard solutions of amino acids purchased from Sigma-Aldrich and containing 2.5  µmol/mL in 0.1 M HCl of each amino acid, alanine (Ala), glycine (Gly), valine (Val), leucine (Leu), isolucine (Ile), proline (Pro), hydroxyproline (Hyp), serine (Ser), threonine (Thr), tyrosine (Tyr), methionine (Met), cysteine (Cys), arginine (Arg), phenylalanine (Phe), histidine (His), aspartic acid (Asp), and glutamic acid (Glu), except for cystine (CySS) whose concentration was 1.25  µ mol/mL. Norleucine (Nor) was used as an internal standard (I.S.2). Animal glue was supplied by Kremer Pigmente GmbH & Co. KG (Germany), while egg yolk and milk have been procured by the authors.

2.2. Apparatus

Microwave oven model Ethos (Milestone) and GC system gas chromatograph coupled with a 5973 inert single quadrupole mass spectrometer (Agilent Technologies) and equipped with a split/splitless injector were used. The MS transfer line temperature was 280°C, the MS ion source temperature was kept at 230°C, and the MS quadrupole temperature was at 150°C. The mass spectrometer operated in the EI positive mode (70 eV). For the gas chromatographic separation, an HP-5MS fused silica capillary column (5% diphenyl/95% dimethyl-polysiloxane, 30 m, 0.25 mm i.d., 0.25  µ m film thickness, J&W Scientific, Agilent Technologies, Palo Alto, CA) with a deactivated silica precolumn (3 m, 0.25 mm i.d., Supelco) was used. The carrier gas (He, purity 99.9995%) was used in the constant flow mode at 1.2 mL/min. For the amino acid analysis, the injector was used in a splitless mode at 250°C. The chromatographic oven was programmed as follows: initial temperature 100°C, isothermal for 2 min, then 4°C/min up to 280°C, and isothermal for 15 min. MS spectra were recorded in a TIC (total ion current) mode.

2.3. Mass Spectra Assignment

Mass spectral assignment was based on the direct match with the spectra of both NIST 2002 library and the laboratory library comprising about 200 mass spectra of pure compounds used in art materials.

2.4. Samples

2.4.1. Reference Materials

The method was tested also on reference materials for the identification of proteinaceous binders on the basis of the quantitative determination of the amino acid profile. Whole egg (We), animal glue (Ag), and whole milk (Wm) were applied on untreated wood specimens (Table 1); whole egg was slightly beaten to obtain a homogeneous mixture, animal glue was dissolved in warm water, and whole milk was used without any treatment. All solutions were applied alone with a brush on test specimens.

The characterization of proteinaceous material can be affected by inorganic compounds derived from the preparatory or from the pictorial layers. For this reason, interference due to inorganic material was evaluated as follows: approximately 1 part of pigment powder (azurite Cu ₃(CO ₃) ₂(OH) ₂, minium Pb ₃O ₄, and Prussian blue Fe ₄[Fe(CN) ₆] ₃) was added to 2 parts of fluid binder (whole egg, animal glue, and milk) and homogenated (Table 1). For a better comprehension of the pigment-binding interaction mechanisms that occur in general in the real samples, Fe and Ca interference has been studied also using a specimen prepared by mixing red ochre with the different binders (whole egg, animal glue, and whole milk), which were applied on wood specimens primed by a layer of gypsum and animal glue: gypsum and animal glue were mixed in 1 : 1 ratio, and red ochre and binder were in 1 : 2 ratio.

2.4.2. Santa Maria delle Cerrate

The method was also applied on a sample of the wall painting of the Church of Santa Maria delle Cerrate (Puglia, Italy). The decoration of the Church, according to written sources, was built in the second half of the 12th century, and it exhibits stylistic aspects related to Greek-byzantine tradition. The pictorial apparatus develops on central apse and lateral aisles. In particular, the sample was collected from the Saint depicted on the wall of the central apse of the Church, and its weight was 1.8 mg. A multistep chemical sample pretreatment based on the ammonia extraction was required to obtain the proteinaceous fraction (together with the polysaccharide fraction) and to separate it from the lipid-resinous fraction [ 31].

2.5. Analytical Procedure

The analytical procedure described in [(i) The ammonia extract containing proteins is evaporated to dryness under a stream of nitrogen and then subjected to acidic hydrolysis assisted by microwave (power 500 W) in the vapor phase with 50 (ii) Bidistilled water (300 (iii) The hydrolysate is evaporated to dryness under a stream of nitrogen (repeated twice by adding a few drops of acetone) and amino acid silylated with 20 (iv) 2  22] has been used to separate protein, lipid, and carbohydrates from the same microsample by GC/MS. Quantification of lipid and carbohydrate was carried out as described in the same paper, whereas protein extracts were treated as follows: µl of 6 N HCl at 160°C for 40 min. µL) and then 5  µL of µL of the solution of derivatized amino acids are analyzed by GC/MS. N-tert-butyldimethylsilyl- N-methyltrifluoroacetamide (MTBSTFA), 73  µL of DMF (solvent), 2  µL of triethylamine (catalyst), and 5  µL of hexadecane solution at 60°C for 30 min. µ L of norleucine (Nor) are added to the acidic hydrolysate.

These steps are outlined in Figure 1.

2.6. Method Validation

The amino acid stock solution was diluted to obtain a calibration curve ranging from 5 nmol/ µL to 30 nmol/ µL. Six calibration levels were used, each of which was injected three times. Nor was added to each diluted standard mixture (100 nmol/ µL final concentration) as IS. The calibration curve was constructed by plotting the ratio analyte peak area/IS peak area versus analyte/IS concentration ratio for each individual analyte. Lack-of-fit test was used to prove the linear range. The instrumental limit of detection (LOD) and limit of quantification (LOQ) of each amino acid was determined as the minimum amount of analyte producing a chromatographic peak with a signal-to-noise ratio equal to 3 and 10, respectively. Six replicate measurements of reagent blanks spiked with low concentrations of analytes (1 nmol/ µL) were performed to obtain the S/ N.

The repeatability and the recovery of the method have been calculated on reference materials. In particular, reference materials were analyzed ten times on the same day and the repeatability was expressed in terms of relative standard deviation (RSD, %), while recoveries were calculated using the following formula: % recovery = 100 × (amount of amino acid determined using DMF solvent)/(amount of amino acid determined using Py solvent). The interference was calculated as recovery (%) = 100 × (amount of amino acids detected in the slurry of the binder and the inorganic material)/(amount of amino acids detected in the binder).

3. Results and Discussion

To evaluate the linearity of the GC/MS method, variable volumes of an amino acid standard mixture were separately evaporated and derivatized, using pyridine and DMF as solvents, and the dilution series of amino acids derivatives were subsequently analyzed. The calibration plots showed good linearity for all compounds tested with DMF and pyridine (Table 1). The use of pyridine as solvent gave a linear response with correlation coefficient ( r ²) values ranging from 0.71 to 0.99. Also, the technique based on the use of DMF as solvent gave linear response in the range 5 nmol/ µL to 30 nmol/ µL with r ² values ranging from 0.80 to 0.99 and instrumental limits of detection ranging from 0.03 to 0.37 ng for the different amino acids. However, comparing the values of the correlation coefficient ( r ²) obtained with the two different methods of derivatization, it is observed that for some amino acids, such as proline and hydroxyproline, the r ² is greater when we use the DMF. In fact, r ² of the proline is 0.89 with pyridine and 0.99 with DMF, while r ² of the hydroxyproline is 0.72 with pyridine and 0.95 with DMF (Table 2). In Figure 2 is shown a typical chromatogram, where the peaks of all amino acids derivatized with DMF as solvent are resolved and easily identifiable.

The repeatability was <20.0% for each of the compounds and recoveries were between 81% and 105%. Another important advantage of the derivatization with DMF as solvent is that it is not mandatory to employ methods of sample cleanup on real pictorial samples containing protein binders. High concentrations of inorganic salts present in the pictorial samples can produce chromatographic interference that inhibits the correct identification of the proteins [ 3, 32, 33]. Recent studies have shown the effectiveness of desalting and concentration techniques based on the pipette tip cleanup [ 34, 35]. The cleanup step is based on the fact that proteins are bound to the stationary phase of the pipette tip, whereas inorganic salts are unretained. The derivatization with DMF as solvent allows to obtain a significant increase of the signal-to-noise ratio without the use of cleanup treatments of proteins reducing the time for preparation and analysis of the samples. As evidenced by reference material analysis, the presence of pigments, present as salts or oxides (azurite, minium, Prussian blue, red ochre, and gypsum), and added to whole egg, animal glue, and whole milk, did not affect recoveries of amino acids that remained in the range of 74-109% nor the relevant profiles. Amino acid content of the analyzed mixtures, expressed as relative percentages, is summarized in Table 3.

The procedure was also effective on a real sample collected from the wall paintings of the Church of Santa Maria delle Cerrate (Italy) and derivatized with the proposed method (Figure 3) [ 36]. The results of the analysis are presented in Table 4 where the amino acid composition (w/w%) of proteins extracted from the wall painting sample is reported along with literature data of proteinaceous binding media such as egg, animal glue, and milk [ 37]. The correlation coefficients among the unknown sample and reference proteinaceous materials ( r ²) strongly suggest the use of eggs as a binding medium in the sample as r ² was equal to 0.95. This is confirmed also by the presence of cholesterol in trace, the ratio azelaic acid/palmitic acid (A/P) < 0.1 [ 2, 5], and by the Σ D% < 1.5 [ 5, 32].

4. Conclusion

Proteinaceous paint media especially those based on egg and milk have been used over the centuries by painters due to excellent optical features and stability of the paint films obtained. Chemical characterization of these materials present in the pictorial samples provides a better knowledge of the artistic heritage and allows us to act during restoration and conservation correctly. The GC/MS analytical techniques are best suited for identifying the binding protein used in cultural heritage, and it is based on hydrolysis and derivatization processes. In this paper, the amino acid fraction, obtained following the procedure described above and reported in the literature [ 22], is derivatized using anhydrous dimethylformamide instead of pyridine as solvent. The enhanced GC/MS procedure for the identification of proteins was validated, and the method demonstrated was precise and accurate. The linearity was tested in the same range for both methods and R ² coefficients were confrontable while LODs and LOQs calculated for DMF solvent were better than values obtained with Py solvent. Overall, the results show that using DMF as solvent, we can limit the formation of analytical artifacts, get clean chromatograms, and improve the LOQ of hydrophilic amino acids such as proline and hydroxyproline. Sample cleanup treatments could be avoided, thus reducing the analysis time: interference of inorganic salts appears not so severe as to prevent recognition of proteinaceous binders. The protocol was successfully tested on reference materials and on a sample collected from the wall paintings of the Church of Santa Maria delle Cerrate (12th century, Italy).

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This work was carried out in the framework of the PRIN 2010-11 (Project no. 2010329WPF_006) funded by the Italian Ministry of University and Research. The financial contribution of the research laboratory network of the Apulia Region "Applied Innovation Technologies for Diagnosis and Conservation of Built Heritage (A.I.Te.C.H.)," PO Puglia FESR 2007-2013, Asse I, Linea 1.2-PO Puglia FSE 2007-2013 Asse IV, is gratefully acknowledged.

# data[].attributes.attachment_link.title
An Enhanced GC/MS Procedure for the Identification of Proteins in Paint Microsamples

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The chemical characterization of materials used in works of art is extremely useful for gaining a better knowledge of the artistic heritage and to guarantee its preservation. A derivatization GC/MS procedure for the identification of proteins in a microsample from painted works of art has been optimized.

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Gas chromatography-mass spectrometry-based analytical strategies for fatty acid analysis in biological samples

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Gas chromatography-mass spectrometry-based analytical strategies for fatty acid analysis in biological samples

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Highlights

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Fatty acid extraction and derivatization approaches are reviewed.

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Pros and cons of different derivatization methods are evaluated.

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Fatty acids separation performances on different GC columns are compared.

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Fatty acid analysis methods applied in various biomedical studies are summarized.

Abstract

Fatty acids play critical roles in biological systems. Imbalances in fatty acids are related to a variety of diseases, which makes the measurement of fatty acids in biological samples important. Many analytical strategies have been developed to investigate fatty acids in various biological samples. Due to the structural diversity of fatty acids, many factors need to be considered when developing analytical methods including extraction methods, derivatization methods, column selections, and internal standard selections. This review focused on gas chromatography-mass spectrometry (GC-MS)-based methods. We reviewed several commonly used fatty acid extraction approaches, including liquid-liquid extraction and solid-phase microextraction. Moreover, both acid and base derivatization methods and other specially designed methods were comprehensively reviewed, and their strengths and limitations were discussed. Having good separation efficiency is essential to building an accurate and reliable GC-MS platform for fatty acid analysis. We reviewed the separation performance of different columns and discussed the application of multidimensional GC for improving separations. The selection of internal standards was also discussed. In the final section, we introduced several biomedical studies that measured fatty acid levels in different sample matrices and provided hints on the relationships between fatty acid imbalances and diseases.

View Abstract

# data[].attributes.attachment_link.title
Gas chromatography-mass spectrometry-based analytical strategies for fatty acid analysis in biological samples

# data[].attributes.attachment_link.description
Fatty acid extraction and derivatization approaches are reviewed. * Pros and cons of different derivatization methods are evaluated. * Fatty acids separation performances on different GC columns are compared. * Fatty acid analysis methods applied in various biomedical studies are summarized.

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Simultaneous analysis of metabolites in potato tuber by gas chromatography –mass spectrometry

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Simultaneous analysis of metabolites in potato tuber by gas chromatography –mass spectrometry

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Summary

A new method is presented in which gas chromatography coupled to mass spectrometry (GC-MS) allows the quantitative and qualitative detection of more than 150 compounds within a potato tuber, in a highly sensitive and specific manner. In contrast to other methods developed for metabolite analysis in plant systems, this method represents an unbiased and open approach that allows the detection of unexpected changes in metabolite levels. Although the method represents a compromise for a wide range of metabolites in terms of extraction, chemical modification and GC-MS analysis, for 25 metabolites analysed in detail the recoveries were found to be within the generally accepted range of 70-140%. Further, the reproducibility of the method was high: the error occurring in the analysis procedures was found to be less than 6% for 30 out of 33 compounds tested. Biological variability exceeded the systematic error of the analysis by a factor of up to 10. The method is also suited for upscaling, potentially allowing the simultaneous analysis of a large number of samples. As a first example this method has been applied to soil‐ and in vitro ‐grown tubers. Due to the simultaneous analysis of a wide range of metabolites it was immediately apparent that these systems differ significantly in their metabolism. Furthermore, the parallel insight into many pathways allows some conclusions to be drawn about the underlying physiological differences between both tuber systems. As a second example, transgenic lines modified in sucrose catabolism or starch synthesis were analysed. This example illustrates the power of an unbiased approach to detecting unexpected changes in transgenic lines.

Introduction

A central interest of our laboratory relates to the understanding of carbohydrate metabolism using the potato as a model plant. To this end we have created numerous transgenic plants characterized by either antisense inhibition or ectopic overexpression of proteins mediating pathways of carbohydrate metabolism. In order to understand better the complexity of events occurring at the metabolite level, it is also necessary to analyse various stages of development, looking at metabolites in many different pathways.

We therefore decided to develop a method that would allow the rapid, highly sensitive and quantitative simultaneous analysis of various metabolites indicative for carbon and nitrogen metabolism, such as sugars, sugar alcohols, dimeric and trimeric saccharides, amines, amino acids and organic acids. In addition, this method should be open to the range of metabolites analysed, and should be suitable for the development of automatic compound identification and quantification.

Here we present an approach essentially based on gas chromatography linked to mass spectrometry (GC-MS), which goes a long way towards meeting the above criteria. When applied to the polar phase of potato tuber extracts, this method allows the detection of a total of 150 compounds in a single run of 1 h duration. Among these 150 compounds we identified 77 with respect to their chemical nature. The reproducibility of this method was high: the cumulative standard deviation was 6% or less for 30 out of 33 compounds tested with respect to the extraction, chemical modification and GC-MS analysis steps. For a wide range of compounds the biological variability was significantly in excess of the variability introduced by the method.

As a first example we applied this analytical approach to two types of potato tuber: soil‐grown, and artificially induced in tissue culture. Whereas earlier data have suggested that these two systems are highly comparable, the broad metabolic profiling method described here reveals major and significant differences between the two systems.

We subsequently analysed a range of transgenic plants displaying a modified sucrose or starch metabolism. In addition to the insights provided by the profiles, the identification of several disaccharides including trehalose, maltose and isomaltose, and sugar alcohols including maltitol, specifically in individual transgenic lines illustrates the power of open metabolic profiling for detecting unexpected events.

Results and Discussion

Rationale for developing an analytical system based on GC-MS

Analysis of metabolites in plants is still often performed using either specific enzyme assays or chromatographic separations such as GC or HPLC, which take retention time/coelution as the main parameter for compound identification. Although powerful, these approaches suffer from at least two drawbacks: they represent non‐open, biased approaches (the investigator will only find information about the metabolite that was experimentally targeted by the particular analysis protocol); and they are labour intensive because the detection systems used either provide information for only a single compound per assay (as is typical for enzyme‐based metabolite assays) or, in the case of chromatographic separation coupled to non‐specific detection, can only be applied to mixtures of low complexity, which in turn often necessitates clean‐up steps.

Therefore approaches should be developed that combine high sensitivity and high specificity (such as that achieved by enzyme tests) with the potential to accommodate the analysis of highly complex mixtures of compounds. Mass spectrometry obviously meets these requirements as it combines high specificity, based upon compound specific fragmentation patterns, with high sensitivity. It has also been used extensively in plant sciences, although as a rule it has been applied for identifying a small number of specific compounds or for identification of compounds in extracts displaying a low complexity (e.g. Delarue et al. 1998; Kamboj et al. 1999; Yamaguchi et al. 1990). In order to apply the selectivity of mass spectrometry to complex mixtures, a previous chromatographic separation is required. For this purpose either GC, liquid chromatography (LC) or capillary electrophoresis can be linked to mass spectrometric detection (e.g. Godber & Parsons, 1998; Katona et al. 1999; Prinsen et al. 1998; Tanaka et al. 1998). In order to automatically identify a compound in a complex mixture the retention time is an essential parameter, as the analysis software needs to be able to limit the window within which it searches for a particular mass spectrum. Therefore reliability and reproducibility of retention time under given conditions is of crucial importance in the choice of separation method.

Comparison of the three methods mentioned above revealed that GC separation best fulfilled the criterion of high reproducibility in retention time for a set of given compounds. For this reason we decided to develop a method based GC separation and linked to a quadruple mass detector. However, GC-MS is not sufficient for a comprehensive analysis of plant metabolites as it is limited to those classes of compounds that are or can be made volatile, and thus can pass the GC separation under the conditions applied. There is undoubtedly scope for profiling methods to be developed for the more recent LC-MS technologies.

Extraction of polar metabolites from potato tubers followed by methoximation and silylation results in complex and reproducible GC-MS chromatograms of >150 compounds

Metabolites present in organisms such as higher plants are composed of multiple classes of chemical compounds. In order to be able to identify and quantify as many different metabolites as possible by the most reliable and least labour‐intensive method, we tested a variety of extraction and chemical modification methods.

With respect to extraction, the addition of methanol to frozen plant material followed by a short heat treatment was found to give the most satisfying results. The combination of methanol and high (70°C) temperature is known to inactivate a majority of enzymes in several systems ( Bligh & Dyer, 1959; Katona et al. 1999), which is a necessary prerequisite to preventing changes in metabolite composition due to enzymatic conversions in the homogenate.

In order to make various classes of compounds volatile and thus accessible for analysis by GC, modification of polar functional groups is necessary. To find the most efficient derivatization reagents capable of working with a wide range of compound classes, we tested several trimethylsilylation reagents: N, O‐bis(trimethylsilyl)acetamide (BSA); trimethylsilylimidazole (TMSI); bis(trimethylsilyl)trifluoroacetamide (BSTFA); 1,1,1,3,3,3‐hexamethyl‐ disilazane (HMDS); and N‐methyl‐ N‐(trimetylsilyl)trifluoroacetamide (MSTFA) ( Katona et al. 1999). Among those reagents, MSTFA gave the best results with the broadest range of chemical compounds and produced the least by‐products. Thus all subsequent silylation reactions were carried out using this reagent.

The carbonyl groups in sugars and sugar derivatives are another functional group requiring modification before GC-MS analysis. Methoxymation prevents ring formation by reducing sugars and stabilizes carbonyl moieties in the β‐position, and has been described as a suitable approach ( Schweer, 1982). We therefore studied different conditions for the methoximation reaction. Methoximation produces two different stereoisomers that were resolved by our chromatography. It is vital that the methoximation reaction proceeds to completion, otherwise a third peak representing the non‐methoxyaminated substance will appear. For this reason, reaction times and temperatures of the methoximation procedure were optimized. The best results were obtained following incubation with methoximation reagent for 90 min at 30°C.

The chemically modified extracts were subsequently subject to GC-MS analysis, leading to peak resolution and signal responses that were acceptable for both qualitative identification and quantification purposes. A typical example of a GC-MS total ion chromatogram from the polar phase of a methanol of greenhouse‐grown potato tubers is shown in Fig. 1.

In Fig. 1(a) complex pattern of major, minor and trace peaks can be observed. If the amount of a specific compound exceeds the column capacity or the linear response range of the detector then a subsequent analysis should be performed with a higher sample dilution.

When two compounds coelute, differences in the respective mass spectra (leading to different ions specific for the compounds in question) can be used selectively to quantify both compounds. Sensitivity can be increased further by running the quadrupole mass detector in the single ion‐monitoring mode. In this mode only a small mass range is analysed, and many more scans are performed per time unit resulting in an approximately 30‐fold increase in sensitivity for a given m/ z ratio.

77 compounds of known chemical structure can be identified in the polar fraction of potato tuber extracts

As shown in Fig. 1 the total ion chromatogram displays the peak profile of a complex mixture. In order to identify the chemical nature of as many peaks as possible, two strategies were followed. First, the spectra of all identifiable peaks were compared with commercially available electron impact mass spectrum libraries such as NIST for Masslab (Fisons, Manchester, UK) or WILEY (Palisade Cooperation, New York, USA). Secondly, GC-MS analysis was performed using several hundred standard compounds which we assumed to be present in detectable amounts in plant tissues, thus creating our own reference library containing both the retention index of these compounds (as determined under our conditions) and the corresponding mass spectrum.

When these approaches were applied to extracts from potato tubers of non‐genetically modified reference plants and to various transgenic lines, we could identify a total of 77 compounds ( Table 1). However, there were still a large number of peaks which were not found either in any of the commercial libraries or in the several hundred compounds that we tested directly. These peaks will require significant further efforts before a chemical name and structure can be assigned to them. The mass spectra of all standard compounds that were processed can be found at http://www.mpimp‐golm.mpg.de/willmitzer/index‐e.html.

Amino acids

Organic acids

Sugars

Sugar alcohols

Aromatic amines

l‐hydroxyproline (230)

3‐phospho‐glycerate (299)

serine (116)

threonine (291)

tryptophane (202)

tyrosine (218)

valine (144)

Identification was performed by demonstration of cochromatography of a standard substance and by comparison of the mass spectrum to the standard (see Experimental procedures). The m/z value indicated after each compound is the specific ion used for quantifying each metabolite. GABA, gamma‐amino butyric acid.

As shown in Table 1, we were also able to detect various sugar phosphates. Although surprising at first glance due to the assumed low volatility of these substances, the mass spectrum clearly identified the phosphorylated compounds due to the presence of an M‐15 fragment peak at m/ z = 706 for both glucose‐6‐phosphate and fructose‐6‐phosphate. The appearance of free phosphate‐TMS esters ( m/ z  = 314) at the same retention time further indicates that intact sugar phosphates enter the ionization source.

Variability caused by extraction, chemical modification and analysis by GC-MS is small when compared to the biological variability within samples

An essential factor in assessing the quality of analytical processes is the reproducibility of results. In order to test our system with respect to this parameter, we divided the analytical process into three components that contribute to the observed variability. These were variability caused by: (i) the final analysis (chromatography, detection, stability of chemically modified samples); the sample preparation; the biological material.

To gain an insight into the reproducibility of the final analysis under authentic conditions, a single chemically modified sample composed of 23 representative standard substances aliquoted in separate measurement vials was measured every 2 h over a 30 h period (the processing time of a typical series of 30 samples).

In this experiment most compounds yielded stable results. In general a loss of only 2% of the initial signal was observed by the end of the 30 h analysis period (data not shown). The only exceptions were glutamine, tryptophane and glycerol, where losses of 24, 10 and 9%, respectively, were observed. We therefore conclude that under typical conditions the reliability of the method is very high.

Before the samples are injected into the GC-MS they undergo a complex series of extraction and chemical modification steps. To see how these steps influence the variability of the results obtained, a single tuber extract was divided into 20 aliquots, chemically processed and finally analysed by GC-MS. Analysis of the data shown in Table 2 demonstrate that the standard deviation introduced by these steps is, as a rule, below 6% of the mean. This was the case for 29 of the 33 metabolites tested in this experiment.

Derivative a

Response ratio b

SD

SD (%) (n = 20)

Tuber extracts from 6 representative developing tubers were pooled and divided into 20 aliquots. Each aliquot was derivatized and analysed as described in Experimental procedures. MEOX, methoxyaminated derivative; TMS, trimethylsilylated derivative; SD, standard deviation.

a Derivatives were per‐trimethylsilylated if not otherwise indicated.

b Response ratios represent peak area ratios using ribitol as quantitative internal standard.

As both of these analyses demonstrate the robustness and high reproducibility of the analytical method per se, we decided to test the biological variability of samples. Tuber slices from nine individual wild‐type plants grown side‐by‐side under identical conditions were analysed. Under ideal growth conditions the metabolites display standard deviations below 20% of the mean ( Table 3) with only two exceptions, glucose and fructose, which are well known to demonstrate a higher variability in potato tubers ( Merlo et al. 1993; Veramendi et al. 1999a; Veramendi et al. 1999b). In most cases biological variability exceeded the experimental error by at least a factor of two (observed range 1.5‐ to 10‐fold).

Derivative a

Relative response ratio b(g−1 FW)

SD a(n = 9)

% a

Samples were obtained from nine wild‐type potato tubers of a single harvest. Extraction, derivatization, and analysis were performed as described in Experimental procedures.

a Derivatives were per‐trimethylsilylated if not otherwise indicated.

b Response ratio was normalized with respect to the fresh weight of the tuber sample.

We therefore conclude that with the GC-MS technology the variability in results is essentially due to the variability within the biological samples themselves.

GC-MS allows both absolute and relative determination of the compounds detected

In order to extend the evaluation of the suitability of this GC-MS approach for metabolic profiling, we quantified representative metabolites in potato tubers. We established calibration curves for 33 compounds, members of five chemical classes: amino acids, organic acids, sugars, saccharides and sugar alcohols. A linear relationship covering the normal concentration range present in plant tissues was observed (data not shown).

These metabolites were then quantified in developing potato tubers ( Table 4); the resulting absolute levels with respect to fresh weight were found to be in the same range as previously reported by both our and other groups using enzymatically linked photometric assays or HPLC analysis of extracts ( Burrell et al. 1994; Geigenberger et al. 1998; Sweetlove et al. 1996; Trethewey et al. 1998). Thus we judged this technology valid. The only major exception observed was with respect to the citric acid content. Citric acid levels were approximately 10 times higher using the GC-MS approach in comparison with previous studies using spectrophotometric‐based methods. We were not able to determine the reason for this discrepancy.

Metabolite

Concentration(μmol g−1 FW)

SE (n = 6)

%

Single tuber samples of six plants were measured. Developing tubers were harvested during the spring season from 10‐week‐old greenhouse plants grown in 2 l pots. SE, Standard error.

In addition to the quantitative measurements, we performed a series of recovery experiments for 25 metabolites drawn as representatives of these five compound classes, and found that the calculated recoveries were within the generally accepted range for analysis work at 70-140% (see Experimental procedures). Taking together the results of the analysis of variability, the data from the recovery experiments, and the comparability of the absolute values determined with previous studies, we conclude that the GC-MS‐based approach is valid for the study of metabolism in potato tubers.

In many cases absolute concentrations are not of prime importance. For the analysis of a special environmental or developmental situation, or for the comparison of a specific genotype with standard tissue samples, relative data are sufficient. The current profiling techniques applied in RNA expression analysis provide only relative data. The determination of relative values can be easily achieved by the GC-MS method as well. For this purpose, within each chromatogram the peak areas derived from specific ion traces indicative of each analysed compound are normalized by the peak area derived from an internal standard, such as ribitol, present within the same chromatogram, resulting in response ratios for all compounds analysed (see Experimental procedures). The response ratios are subsequently converted to relative response ratios through division by the fresh weight of each sample, thus achieving further normalization. These relative response ratios can be directly compared between different samples without knowledge of absolute compound concentrations. The specific ion masses used for the respective analyte for quantification are given in Table 1.

Determination of both relative values and absolute concentrations have advantages and disadvantages. However, for comparative purposes between two sample types we routinely describe changes by calculating the quotients of the various relative response ratios for each compound.

Automization of the chromatogram analysis

It is obvious from the preceding descriptions that evaluation by eye of each single chromatogram would be an impossible task and would represent a major stumbling block for the efficient use of this technology. We therefore decided to use a chromatography data analysis algorithm that allows multiparallel and automatic identification and quantification of compounds present in a GC-MS chromatogram. This basic algorithm, which is part of the masslab software distributed by ThermoQuest (Manchester, UK), allows the use of two strategies for identification of a compound and quantification of a set of given compounds. For identification, the first parameter used by the software is the retention time of the compound relative to a standard compound. Initially we used just one compound for determination of relative retention times; however, this proved insufficient as non‐linear shifts of retention times occur in the elution profiles during the lifetime of a GC column. We therefore decided to spike every sample before injection with several compounds that were absent from the fraction under analysis. For the work presented here we used fatty acids ( Fig. 1); the use of fatty acids has been described as an alternative approach to using n‐alkanes for determining the Kovats index ( Castello, 1999; Gonzalez & Nardillo, 1999). Subsequently the relative retention time of each compound is interpolated with respect to its two nearest neighbours and taken as the first identification criterion. As a second subsequent criterion we used the full electrone impact ionization (EI) mass spectrum for the compounds in question. A match factor was automatically calculated which provides an indication of the reliability of assignment. In other words, a confidentiality index is provided which alerts the investigator when manual inspection of the compound assignment is necessary.

For quantification purposes a single specific ion known to be selective and sensitive for the compound in question was taken, and the response ratio calculated as described above. For the analysis presented here ribitol was used as the quantitative internal standard (see Experimental procedures).

Applying this method to potato tuber extracts, we can currently identify and automatically quantify 60 of the 77 known compounds with a very high fidelity, i.e. less than 0.1% manual inspection. The remaining 18 compounds can be quantified, but this process requires at least 5% reassessments of the original chromatogram by a trained user. It is to be expected that with the development of improved software and GC-MS hardware the number of compounds requiring supplementary manual assessment will decrease.

Metabolic profiling using GC-MS reveals major differences between soil‐ and in vitro‐grown tubers

When analysing the growth and development of plant systems, in vitro culture is an interesting technology as it allows the manipulation of environmental conditions in a defined manner. With respect to potato tubers, numerous reports including work from our own group have described the suitability of in vitro‐grown tubers for the analysis of gene expression or changes in metabolite composition. Morphological, molecular and biochemical data appear to be in agreement with the assumption that in vitro‐grown tubers represent a faithful phenocopy of soil‐grown potato tubers ( Debon et al. 1998; Desire et al. 1995; Veramendi et al. 1999a; Visser et al. 1994). In view of the compelling evidence for the equivalence of these systems, we decided to apply the metabolic profiling approach in order to assess a broader range of compounds using this unbiased approach.

Data describing the ratios of many metabolites in both systems are summarized in Fig. 2, which shows that in vitro‐ and soil‐grown potato tubers are not as similar as previously thought. Major differences were found in the group of amino acids, most notably in glutamic acid and other amino acids derived from α‐ketoglutaric acid such as glutamine, proline and arginine. In addition, large increases were observed in the levels of amino acids derived from oxaloacetic acid, such as asparagine and lysine, within in vitro‐grown tubers. However, tyrosine, glycine, alanine, β‐alanine and phenylalanine decreased in in vitro‐grown when compared to soil‐grown tubers. In general, microtubers were found to contain a much higher amount of amino acids in comparison to soil‐grown tubers. A high anaplerotic demand for carbon may explain the decreases observed in citric acid‐cycle intermediates such as citric acid, malic acid, succinic acid and isocitric acid. Alternatively, many of these changes could be a consequence of the higher amount of nitrate supplied to the microtubers. As the in vitro‐grown tubers are probably not limited in carbohydrate supply due to the large amount of sugars present in the tuber‐inducing medium, the rate of nitrate assimilation may be markedly increased in comparison with soil‐grown tubers ( Morcuende et al. 1998).

Another important difference between both types of tubers is the higher amount of compounds indicative of osmotic stress found in the in vitro tubers ( Hare et al. 1998; Hare et al. 1999; Karakas et al. 1997). For example, glycerol, mannitol, inositol, and proline are significantly increased. Finally, some of the changes seen in the amino acid pools, such as the increase in asparagine, glutamine, and glutamic acid, have been observed in water‐stressed leaves of tomato ( Bauer et al. 1997), sugar beet ( Gzik, 1996) and mulberry ( Ramanjulu & Sudhakar, 1997). The reason for the possible water stress could be the high amount of sucrose present in the tuber‐inducing medium.

In conclusion, the application of a broad profiling method to two systems previously thought to be very comparable reveals that caution must be exercised when interpreting biological events by the analysis of a restricted number of parameters.

Metabolic profiling of several transgenic lines modified in either sugar or starch metabolism reveals unexpected changes in disaccharides and sugar alcohols

In order to explore further the power of metabolic profiling for detecting unexpected changes, tubers from three transgenic and one wild‐type line were subjected to GC-MS analysis, as described above. The transgenic lines analysed were either altered in sucrose metabolism (expression of a yeast invertase in either the cytosol or apoplast ( Sonnewald et al. 1997; Trethewey et al. 1998), or inhibited in starch biosynthesis following antisense RNA‐mediated repression of the activity of ADP‐glucose pyrophosphorylase ( Müller‐Röber et al. 1992).

GC-MS analysis revealed that amongst the metabolites analysed a large fraction (33) were significantly changed in at least one of the transgenic lines. A comparison of the data obtained for the three transgenic lines using GC-MS profiling with the values previously published from HPLC analysis for amino acids and organic acids ( Trethewey et al. 1998; Trethewey et al. 1999) shows that the profiling data are in broad agreement with the previously determined values ( Fig. 3). Our general experience suggests that the differences observed in the extent of quantitative changes are due to the normal differences between the growing conditions of different batches of tubers.

All the scientific conclusions previously drawn about the redistribution of metabolism in the transgenic lines can be drawn from this single profiling analysis. In particular, the increase in glycolysis, amino acids and organic acids in the line expressing the yeast invertase in the cytosol can be clearly deducted from the data in Fig. 3. With respect to the transgenic line expressing a yeast invertase in the apoplast (U‐IN1), it is apparent from Fig. 3 that this line does not have an elevated respiratory flux.

With respect to the line expressing the yeast invertase in the cytosol, the appearance of 6‐phosphogluconic acid is a new result and can be taken as a strong indication of a significantly increased level of the oxidative pentose phosphate pathway, which might be expected given the large increase in glucose‐6‐phosphate in this line.

A most remarkable and unexpected finding concerns the presence of dramatically increased levels of disaccharides such as maltose, isomaltose and trehalose in the line expressing the cytosolic invertase. This result has been found in three independent lines (data not shown) Specifically, the presence of trehalose is an exciting observation. In addition to confirming and extending previous observations on the capacity of higher plants to synthesize trehalose under specific conditions ( Goddijn & Smeekens, 1998; Müller et al. 1999), this finding is of relevance as trehalose has been discussed extensively in the literature as a possible signal for the metabolic status of a given cell. The fact that trehalose appears only in those cells where an increased rate of glycolysis and respiration has been observed makes this a fascinating and stimulating observation. Further comparisons with other lines displaying a change in the rate of glycolysis and respiration must reveal whether or not this exciting but nevertheless simple interpretation is true.

The transgenic lines have been analysed in significant detail in earlier publications by applying conventional techniques such as enzyme assays and HPLC. Nevertheless, the occurrence of trehalose and other disaccharides has not been noted, mainly due to the fact that the methods used previously were not capable of determining these compounds. The unbiased approach for metabolic profiling presented here gives a clear advantage in providing the opportunity to find unexpected events, as exemplified by the identification of trehalose, and thus may provide novel insights into metabolic networks.

Conclusions

Using GC-MS as a system for separation and detection of metabolites, we were able to develop a simple procedure allowing the simultaneous analysis of a large group of compounds representing sugars, sugar alcohols, amino acids, organic acids and some special compounds in plant extracts. This method allows an unbiased study of a range of metabolic pathways with only minimal effort. As this technology utilizes MS, it also combines high sensitivity with high specificity and, as shown here, high reproducibility.

We are presently in the process of applying this technique to a number of other plant species. Depending upon the species and the tissue, between 50 and 400 different compounds can be detected when the analysis is applied to hydrophilic and lipophilic classes of compounds. As this system has the potential to be fully automated, we believe that in the future it will represent a major tool for characterizing the metabolic status of a plant with respect to environmental, developmental or genetic factors.

Experimental procedures

Plant material

Potato plants, Solanum tuberosum L. cv. Desirée, were supplied by Saatzucht Lange (Bad Schwartau, Germany). Plants were grown in a greenhouse at 22°C under a 16 h light/8 h dark regime with supplementary artificial light under a minimum of 250 μmol photons m ² sec −1. In this paper the term 'developing tubers' refers to tubers above 10 g that were harvested from 10‐week‐old plants.

Microtubers were generated in vitro as described by Veramendi et al. (1999a). Single‐node explants were cultured in MS media ( Murashige & Skoog, 1962) containing 6% (w/v) sucrose and 11.6 μm kinetin. Sets of 15 explants were kept in the dark in glass jars containing 50 ml MS medium. Microtubers were harvested after 4 weeks at 20°C.

Chemicals

All chemicals and pure standard substances were purchased from either Sigma-Aldrich Chemie GmbH (Deisenhofen, Germany) or Merck KGaA (Darmstadt, Germany).

Extraction and derivatization of potato tuber metabolites for GC-MS analysis

Tuber slices or whole microtubers were immediately frozen in liquid nitrogen and stored at −70°C until further analysis. A polar metabolite fraction was obtained from either approximately 50 mg microtuber or 100 mg tuber slices by Ultra Thurax T25 (IKA Labortechnik, Staufen, Germany), homogenization in 1400 μl 100% methanol with 50 μl internal standard (2 mg ribitol ml −1 water). The mixture was extracted for 15 min at 70°C. The extract was vigorously mixed with 1 vol water and subsequently centrifuged at 2200 g. Aliquots of the methanol/water supernatant (1.00 or 0.25 ml) were dried in vacuo for 6-16 h. The dried residue was redissolved and derivatized for 90 min at 30°C (in 80 μl of 20 mg ml −1 methoxyamine hydrochloride in pyridine) followed by a 30 min treatment at 37°C (with 80 μl MSTFA). 40 μl of a retention time standard mixture was added prior to trimethylsilylation. This retention time standard mixture contained 3.7% (w) heptanoic acid, 3.7% (w) nonanoic acid, 3.7% (w) undecanoic acid, 3.7% (w) tridecanoic acid, 3.7% (w) pentadecanoic acid, 7.4% (w) nonadecanoic acid, 7.4% (w) tricosanoic acid, 22.2% (w) heptacosanoic acid and 55.5% (w) hentriacontanoic acid dissolved in tetrahydrofuran at 10 mg ml −1 total concentration.

Standard substances for peak identification were dissolved in water at 10 mg ml −1. A 5 μl volume of standard solution was dried in vacuo and derivatized with 50 μl of 20 mg ml −1 methoxyamine hydrochloride in pyridine and 50 μl MSTFA, as described above.

To establish the efficiency of the extraction procedure, the recovery of various standard metabolites was determined by the addition of authentic metabolite standards to the tissue sample at the start of the extraction procedure. Standard substances were added in threefold excess of the determined endogenous concentrations. Estimates of recovery were 138% for alanine, 100% for aspartic acid, 107% for glycine, 107% for isoleucine, 99% for leucine, 92% for lysine, 106% for phenylalanine, 90% for proline, 118% for valine, 127% for serine, 110% for fructose, 78% for fructose‐6‐phosphate, 101% for galactose, 129% for glucose, 78% for glucose‐6‐phosphate, 115% for maltose, 100% for sucrose, 101% for inositol, 108% for mannitol, 139% for glyceric acid, 117% for fumaric acid, 98% for isocitric acid, 118% for malic acid, 99% for shikimic acid and 73% for succinic acid.

GC-MS analysis

Sample volumes of 1 μl were injected with a split ratio of 25 : 1 using a hot‐needle technique. The GC-MS system consisted of an AS 2000 autosampler, a GC 8000 gas chromatograph and a Voyager quadrupole mass spectrometer (ThermoQuest, Manchester, UK). The mass spectrometer was tuned according to the manufacturer's recommendations using tris‐(perfluorobutyl)‐amine (CF43). Gas chromatography was performed on a 30 m SPB‐50 column with 0.25 mm inner diameter and 0.25 μm film thickness (Supelco, Bellfonte, CA, USA). Injection temperature was 230°C, the interface set to 250°C and the ion source adjusted to 200°C. The carrier gas used was helium set at a constant flow rate of 1 ml min −1. The temperature program was 5 min isothermal heating at 70°C, followed by a 5°C min −1 oven temperature ramp to 310°C and a final 1 min heating at 310°C. The system was then temperature equilibrated for 6 min at 70°C prior to injection of the next sample. Mass spectra were recorded at two scans per sec with an m/ z 50-600 scanning range. The chromatograms and mass spectra were evaluated using the masslab program (ThermoQuest, Manchester, UK). A retention time and mass spectral library for automatic peak quantification of metabolite derivatives was implemented within the masslab method format.

Statistical analysis

The t‐tests were performed using the algorithm incorporated into Microsoft excel (Microsoft Corporation, Seattle, WA, USA). The word significant is used in the text when the change in question has been confirmed to be statistically significant ( P < 0.05) with the t ‐test.

Acknowledgements

We would like to thank the gardeners for taking excellent care of the greenhouse plants. Our special thanks are assigned to Dr Alisdair Fernie for proofreading the manuscript and his helpful discussions. We would also like to thank Kristina Zubow for her patient and tireless efforts in creating the mass spectral library.

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Summary

A new method is presented in which gas chromatography coupled to mass spectrometry (GC-MS) allows the quantitative and qualitative detection of more than 150 compounds within a potato tuber, in a highly sensitive and specific manner. In contrast to other methods developed for metabolite analysis in plant systems, this method represents an unbiased and open approach that allows the detection of unexpected changes in metabolite levels. Although the method represents a compromise for a wide range of metabolites in terms of extraction, chemical modification and GC-MS analysis, for 25 metabolites analysed in detail the recoveries were found to be within the generally accepted range of 70-140%. Further, the reproducibility of the method was high: the error occurring in the analysis procedures was found to be less than 6% for 30 out of 33 compounds tested. Biological variability exceeded the systematic error of the analysis by a factor of up to 10. The method is also suited for upscaling, potentially allowing the simultaneous analysis of a large number of samples. As a first example this method has been applied to soil‐ and in vitro ‐grown tubers. Due to the simultaneous analysis of a wide range of metabolites it was immediately apparent that these systems differ significantly in their metabolism. Furthermore, the parallel insight into many pathways allows some conclusions to be drawn about the underlying physiological differences between both tuber systems. As a second example, transgenic lines modified in sucrose catabolism or starch synthesis were analysed. This example illustrates the power of an unbiased approach to detecting unexpected changes in transgenic lines.

Introduction

A central interest of our laboratory relates to the understanding of carbohydrate metabolism using the potato as a model plant. To this end we have created numerous transgenic plants characterized by either antisense inhibition or ectopic overexpression of proteins mediating pathways of carbohydrate metabolism. In order to understand better the complexity of events occurring at the metabolite level, it is also necessary to analyse various stages of development, looking at metabolites in many different pathways.

We therefore decided to develop a method that would allow the rapid, highly sensitive and quantitative simultaneous analysis of various metabolites indicative for carbon and nitrogen metabolism, such as sugars, sugar alcohols, dimeric and trimeric saccharides, amines, amino acids and organic acids. In addition, this method should be open to the range of metabolites analysed, and should be suitable for the development of automatic compound identification and quantification.

Here we present an approach essentially based on gas chromatography linked to mass spectrometry (GC-MS), which goes a long way towards meeting the above criteria. When applied to the polar phase of potato tuber extracts, this method allows the detection of a total of 150 compounds in a single run of 1 h duration. Among these 150 compounds we identified 77 with respect to their chemical nature. The reproducibility of this method was high: the cumulative standard deviation was 6% or less for 30 out of 33 compounds tested with respect to the extraction, chemical modification and GC-MS analysis steps. For a wide range of compounds the biological variability was significantly in excess of the variability introduced by the method.

As a first example we applied this analytical approach to two types of potato tuber: soil‐grown, and artificially induced in tissue culture. Whereas earlier data have suggested that these two systems are highly comparable, the broad metabolic profiling method described here reveals major and significant differences between the two systems.

We subsequently analysed a range of transgenic plants displaying a modified sucrose or starch metabolism. In addition to the insights provided by the profiles, the identification of several disaccharides including trehalose, maltose and isomaltose, and sugar alcohols including maltitol, specifically in individual transgenic lines illustrates the power of open metabolic profiling for detecting unexpected events.

Results and Discussion

Rationale for developing an analytical system based on GC-MS

Analysis of metabolites in plants is still often performed using either specific enzyme assays or chromatographic separations such as GC or HPLC, which take retention time/coelution as the main parameter for compound identification. Although powerful, these approaches suffer from at least two drawbacks: they represent non‐open, biased approaches (the investigator will only find information about the metabolite that was experimentally targeted by the particular analysis protocol); and they are labour intensive because the detection systems used either provide information for only a single compound per assay (as is typical for enzyme‐based metabolite assays) or, in the case of chromatographic separation coupled to non‐specific detection, can only be applied to mixtures of low complexity, which in turn often necessitates clean‐up steps.

Therefore approaches should be developed that combine high sensitivity and high specificity (such as that achieved by enzyme tests) with the potential to accommodate the analysis of highly complex mixtures of compounds. Mass spectrometry obviously meets these requirements as it combines high specificity, based upon compound specific fragmentation patterns, with high sensitivity. It has also been used extensively in plant sciences, although as a rule it has been applied for identifying a small number of specific compounds or for identification of compounds in extracts displaying a low complexity (e.g. Delarue et al. 1998; Kamboj et al. 1999; Yamaguchi et al. 1990). In order to apply the selectivity of mass spectrometry to complex mixtures, a previous chromatographic separation is required. For this purpose either GC, liquid chromatography (LC) or capillary electrophoresis can be linked to mass spectrometric detection (e.g. Godber & Parsons, 1998; Katona et al. 1999; Prinsen et al. 1998; Tanaka et al. 1998). In order to automatically identify a compound in a complex mixture the retention time is an essential parameter, as the analysis software needs to be able to limit the window within which it searches for a particular mass spectrum. Therefore reliability and reproducibility of retention time under given conditions is of crucial importance in the choice of separation method.

Comparison of the three methods mentioned above revealed that GC separation best fulfilled the criterion of high reproducibility in retention time for a set of given compounds. For this reason we decided to develop a method based GC separation and linked to a quadruple mass detector. However, GC-MS is not sufficient for a comprehensive analysis of plant metabolites as it is limited to those classes of compounds that are or can be made volatile, and thus can pass the GC separation under the conditions applied. There is undoubtedly scope for profiling methods to be developed for the more recent LC-MS technologies.

Extraction of polar metabolites from potato tubers followed by methoximation and silylation results in complex and reproducible GC-MS chromatograms of >150 compounds

Metabolites present in organisms such as higher plants are composed of multiple classes of chemical compounds. In order to be able to identify and quantify as many different metabolites as possible by the most reliable and least labour‐intensive method, we tested a variety of extraction and chemical modification methods.

With respect to extraction, the addition of methanol to frozen plant material followed by a short heat treatment was found to give the most satisfying results. The combination of methanol and high (70°C) temperature is known to inactivate a majority of enzymes in several systems ( Bligh & Dyer, 1959; Katona et al. 1999), which is a necessary prerequisite to preventing changes in metabolite composition due to enzymatic conversions in the homogenate.

In order to make various classes of compounds volatile and thus accessible for analysis by GC, modification of polar functional groups is necessary. To find the most efficient derivatization reagents capable of working with a wide range of compound classes, we tested several trimethylsilylation reagents: N, O‐bis(trimethylsilyl)acetamide (BSA); trimethylsilylimidazole (TMSI); bis(trimethylsilyl)trifluoroacetamide (BSTFA); 1,1,1,3,3,3‐hexamethyl‐ disilazane (HMDS); and N‐methyl‐ N‐(trimetylsilyl)trifluoroacetamide (MSTFA) ( Katona et al. 1999). Among those reagents, MSTFA gave the best results with the broadest range of chemical compounds and produced the least by‐products. Thus all subsequent silylation reactions were carried out using this reagent.

The carbonyl groups in sugars and sugar derivatives are another functional group requiring modification before GC-MS analysis. Methoxymation prevents ring formation by reducing sugars and stabilizes carbonyl moieties in the β‐position, and has been described as a suitable approach ( Schweer, 1982). We therefore studied different conditions for the methoximation reaction. Methoximation produces two different stereoisomers that were resolved by our chromatography. It is vital that the methoximation reaction proceeds to completion, otherwise a third peak representing the non‐methoxyaminated substance will appear. For this reason, reaction times and temperatures of the methoximation procedure were optimized. The best results were obtained following incubation with methoximation reagent for 90 min at 30°C.

The chemically modified extracts were subsequently subject to GC-MS analysis, leading to peak resolution and signal responses that were acceptable for both qualitative identification and quantification purposes. A typical example of a GC-MS total ion chromatogram from the polar phase of a methanol of greenhouse‐grown potato tubers is shown in Fig. 1.

In Fig. 1(a) complex pattern of major, minor and trace peaks can be observed. If the amount of a specific compound exceeds the column capacity or the linear response range of the detector then a subsequent analysis should be performed with a higher sample dilution.

When two compounds coelute, differences in the respective mass spectra (leading to different ions specific for the compounds in question) can be used selectively to quantify both compounds. Sensitivity can be increased further by running the quadrupole mass detector in the single ion‐monitoring mode. In this mode only a small mass range is analysed, and many more scans are performed per time unit resulting in an approximately 30‐fold increase in sensitivity for a given m/ z ratio.

77 compounds of known chemical structure can be identified in the polar fraction of potato tuber extracts

As shown in Fig. 1 the total ion chromatogram displays the peak profile of a complex mixture. In order to identify the chemical nature of as many peaks as possible, two strategies were followed. First, the spectra of all identifiable peaks were compared with commercially available electron impact mass spectrum libraries such as NIST for Masslab (Fisons, Manchester, UK) or WILEY (Palisade Cooperation, New York, USA). Secondly, GC-MS analysis was performed using several hundred standard compounds which we assumed to be present in detectable amounts in plant tissues, thus creating our own reference library containing both the retention index of these compounds (as determined under our conditions) and the corresponding mass spectrum.

When these approaches were applied to extracts from potato tubers of non‐genetically modified reference plants and to various transgenic lines, we could identify a total of 77 compounds ( Table 1). However, there were still a large number of peaks which were not found either in any of the commercial libraries or in the several hundred compounds that we tested directly. These peaks will require significant further efforts before a chemical name and structure can be assigned to them. The mass spectra of all standard compounds that were processed can be found at http://www.mpimp‐golm.mpg.de/willmitzer/index‐e.html.

Amino acids

Organic acids

Sugars

Sugar alcohols

Aromatic amines

l‐hydroxyproline (230)

3‐phospho‐glycerate (299)

serine (116)

threonine (291)

tryptophane (202)

tyrosine (218)

valine (144)

Identification was performed by demonstration of cochromatography of a standard substance and by comparison of the mass spectrum to the standard (see Experimental procedures). The m/z value indicated after each compound is the specific ion used for quantifying each metabolite. GABA, gamma‐amino butyric acid.

As shown in Table 1, we were also able to detect various sugar phosphates. Although surprising at first glance due to the assumed low volatility of these substances, the mass spectrum clearly identified the phosphorylated compounds due to the presence of an M‐15 fragment peak at m/ z = 706 for both glucose‐6‐phosphate and fructose‐6‐phosphate. The appearance of free phosphate‐TMS esters ( m/ z  = 314) at the same retention time further indicates that intact sugar phosphates enter the ionization source.

Variability caused by extraction, chemical modification and analysis by GC-MS is small when compared to the biological variability within samples

An essential factor in assessing the quality of analytical processes is the reproducibility of results. In order to test our system with respect to this parameter, we divided the analytical process into three components that contribute to the observed variability. These were variability caused by: (i) the final analysis (chromatography, detection, stability of chemically modified samples); the sample preparation; the biological material.

To gain an insight into the reproducibility of the final analysis under authentic conditions, a single chemically modified sample composed of 23 representative standard substances aliquoted in separate measurement vials was measured every 2 h over a 30 h period (the processing time of a typical series of 30 samples).

In this experiment most compounds yielded stable results. In general a loss of only 2% of the initial signal was observed by the end of the 30 h analysis period (data not shown). The only exceptions were glutamine, tryptophane and glycerol, where losses of 24, 10 and 9%, respectively, were observed. We therefore conclude that under typical conditions the reliability of the method is very high.

Before the samples are injected into the GC-MS they undergo a complex series of extraction and chemical modification steps. To see how these steps influence the variability of the results obtained, a single tuber extract was divided into 20 aliquots, chemically processed and finally analysed by GC-MS. Analysis of the data shown in Table 2 demonstrate that the standard deviation introduced by these steps is, as a rule, below 6% of the mean. This was the case for 29 of the 33 metabolites tested in this experiment.

Derivative a

Response ratio b

SD

SD (%) (n = 20)

Tuber extracts from 6 representative developing tubers were pooled and divided into 20 aliquots. Each aliquot was derivatized and analysed as described in Experimental procedures. MEOX, methoxyaminated derivative; TMS, trimethylsilylated derivative; SD, standard deviation.

a Derivatives were per‐trimethylsilylated if not otherwise indicated.

b Response ratios represent peak area ratios using ribitol as quantitative internal standard.

As both of these analyses demonstrate the robustness and high reproducibility of the analytical method per se, we decided to test the biological variability of samples. Tuber slices from nine individual wild‐type plants grown side‐by‐side under identical conditions were analysed. Under ideal growth conditions the metabolites display standard deviations below 20% of the mean ( Table 3) with only two exceptions, glucose and fructose, which are well known to demonstrate a higher variability in potato tubers ( Merlo et al. 1993; Veramendi et al. 1999a; Veramendi et al. 1999b). In most cases biological variability exceeded the experimental error by at least a factor of two (observed range 1.5‐ to 10‐fold).

Derivative a

Relative response ratio b(g−1 FW)

SD a(n = 9)

% a

Samples were obtained from nine wild‐type potato tubers of a single harvest. Extraction, derivatization, and analysis were performed as described in Experimental procedures.

a Derivatives were per‐trimethylsilylated if not otherwise indicated.

b Response ratio was normalized with respect to the fresh weight of the tuber sample.

We therefore conclude that with the GC-MS technology the variability in results is essentially due to the variability within the biological samples themselves.

GC-MS allows both absolute and relative determination of the compounds detected

In order to extend the evaluation of the suitability of this GC-MS approach for metabolic profiling, we quantified representative metabolites in potato tubers. We established calibration curves for 33 compounds, members of five chemical classes: amino acids, organic acids, sugars, saccharides and sugar alcohols. A linear relationship covering the normal concentration range present in plant tissues was observed (data not shown).

These metabolites were then quantified in developing potato tubers ( Table 4); the resulting absolute levels with respect to fresh weight were found to be in the same range as previously reported by both our and other groups using enzymatically linked photometric assays or HPLC analysis of extracts ( Burrell et al. 1994; Geigenberger et al. 1998; Sweetlove et al. 1996; Trethewey et al. 1998). Thus we judged this technology valid. The only major exception observed was with respect to the citric acid content. Citric acid levels were approximately 10 times higher using the GC-MS approach in comparison with previous studies using spectrophotometric‐based methods. We were not able to determine the reason for this discrepancy.

Metabolite

Concentration(μmol g−1 FW)

SE (n = 6)

%

Single tuber samples of six plants were measured. Developing tubers were harvested during the spring season from 10‐week‐old greenhouse plants grown in 2 l pots. SE, Standard error.

In addition to the quantitative measurements, we performed a series of recovery experiments for 25 metabolites drawn as representatives of these five compound classes, and found that the calculated recoveries were within the generally accepted range for analysis work at 70-140% (see Experimental procedures). Taking together the results of the analysis of variability, the data from the recovery experiments, and the comparability of the absolute values determined with previous studies, we conclude that the GC-MS‐based approach is valid for the study of metabolism in potato tubers.

In many cases absolute concentrations are not of prime importance. For the analysis of a special environmental or developmental situation, or for the comparison of a specific genotype with standard tissue samples, relative data are sufficient. The current profiling techniques applied in RNA expression analysis provide only relative data. The determination of relative values can be easily achieved by the GC-MS method as well. For this purpose, within each chromatogram the peak areas derived from specific ion traces indicative of each analysed compound are normalized by the peak area derived from an internal standard, such as ribitol, present within the same chromatogram, resulting in response ratios for all compounds analysed (see Experimental procedures). The response ratios are subsequently converted to relative response ratios through division by the fresh weight of each sample, thus achieving further normalization. These relative response ratios can be directly compared between different samples without knowledge of absolute compound concentrations. The specific ion masses used for the respective analyte for quantification are given in Table 1.

Determination of both relative values and absolute concentrations have advantages and disadvantages. However, for comparative purposes between two sample types we routinely describe changes by calculating the quotients of the various relative response ratios for each compound.

Automization of the chromatogram analysis

It is obvious from the preceding descriptions that evaluation by eye of each single chromatogram would be an impossible task and would represent a major stumbling block for the efficient use of this technology. We therefore decided to use a chromatography data analysis algorithm that allows multiparallel and automatic identification and quantification of compounds present in a GC-MS chromatogram. This basic algorithm, which is part of the masslab software distributed by ThermoQuest (Manchester, UK), allows the use of two strategies for identification of a compound and quantification of a set of given compounds. For identification, the first parameter used by the software is the retention time of the compound relative to a standard compound. Initially we used just one compound for determination of relative retention times; however, this proved insufficient as non‐linear shifts of retention times occur in the elution profiles during the lifetime of a GC column. We therefore decided to spike every sample before injection with several compounds that were absent from the fraction under analysis. For the work presented here we used fatty acids ( Fig. 1); the use of fatty acids has been described as an alternative approach to using n‐alkanes for determining the Kovats index ( Castello, 1999; Gonzalez & Nardillo, 1999). Subsequently the relative retention time of each compound is interpolated with respect to its two nearest neighbours and taken as the first identification criterion. As a second subsequent criterion we used the full electrone impact ionization (EI) mass spectrum for the compounds in question. A match factor was automatically calculated which provides an indication of the reliability of assignment. In other words, a confidentiality index is provided which alerts the investigator when manual inspection of the compound assignment is necessary.

For quantification purposes a single specific ion known to be selective and sensitive for the compound in question was taken, and the response ratio calculated as described above. For the analysis presented here ribitol was used as the quantitative internal standard (see Experimental procedures).

Applying this method to potato tuber extracts, we can currently identify and automatically quantify 60 of the 77 known compounds with a very high fidelity, i.e. less than 0.1% manual inspection. The remaining 18 compounds can be quantified, but this process requires at least 5% reassessments of the original chromatogram by a trained user. It is to be expected that with the development of improved software and GC-MS hardware the number of compounds requiring supplementary manual assessment will decrease.

Metabolic profiling using GC-MS reveals major differences between soil‐ and in vitro‐grown tubers

When analysing the growth and development of plant systems, in vitro culture is an interesting technology as it allows the manipulation of environmental conditions in a defined manner. With respect to potato tubers, numerous reports including work from our own group have described the suitability of in vitro‐grown tubers for the analysis of gene expression or changes in metabolite composition. Morphological, molecular and biochemical data appear to be in agreement with the assumption that in vitro‐grown tubers represent a faithful phenocopy of soil‐grown potato tubers ( Debon et al. 1998; Desire et al. 1995; Veramendi et al. 1999a; Visser et al. 1994). In view of the compelling evidence for the equivalence of these systems, we decided to apply the metabolic profiling approach in order to assess a broader range of compounds using this unbiased approach.

Data describing the ratios of many metabolites in both systems are summarized in Fig. 2, which shows that in vitro‐ and soil‐grown potato tubers are not as similar as previously thought. Major differences were found in the group of amino acids, most notably in glutamic acid and other amino acids derived from α‐ketoglutaric acid such as glutamine, proline and arginine. In addition, large increases were observed in the levels of amino acids derived from oxaloacetic acid, such as asparagine and lysine, within in vitro‐grown tubers. However, tyrosine, glycine, alanine, β‐alanine and phenylalanine decreased in in vitro‐grown when compared to soil‐grown tubers. In general, microtubers were found to contain a much higher amount of amino acids in comparison to soil‐grown tubers. A high anaplerotic demand for carbon may explain the decreases observed in citric acid‐cycle intermediates such as citric acid, malic acid, succinic acid and isocitric acid. Alternatively, many of these changes could be a consequence of the higher amount of nitrate supplied to the microtubers. As the in vitro‐grown tubers are probably not limited in carbohydrate supply due to the large amount of sugars present in the tuber‐inducing medium, the rate of nitrate assimilation may be markedly increased in comparison with soil‐grown tubers ( Morcuende et al. 1998).

Another important difference between both types of tubers is the higher amount of compounds indicative of osmotic stress found in the in vitro tubers ( Hare et al. 1998; Hare et al. 1999; Karakas et al. 1997). For example, glycerol, mannitol, inositol, and proline are significantly increased. Finally, some of the changes seen in the amino acid pools, such as the increase in asparagine, glutamine, and glutamic acid, have been observed in water‐stressed leaves of tomato ( Bauer et al. 1997), sugar beet ( Gzik, 1996) and mulberry ( Ramanjulu & Sudhakar, 1997). The reason for the possible water stress could be the high amount of sucrose present in the tuber‐inducing medium.

In conclusion, the application of a broad profiling method to two systems previously thought to be very comparable reveals that caution must be exercised when interpreting biological events by the analysis of a restricted number of parameters.

Metabolic profiling of several transgenic lines modified in either sugar or starch metabolism reveals unexpected changes in disaccharides and sugar alcohols

In order to explore further the power of metabolic profiling for detecting unexpected changes, tubers from three transgenic and one wild‐type line were subjected to GC-MS analysis, as described above. The transgenic lines analysed were either altered in sucrose metabolism (expression of a yeast invertase in either the cytosol or apoplast ( Sonnewald et al. 1997; Trethewey et al. 1998), or inhibited in starch biosynthesis following antisense RNA‐mediated repression of the activity of ADP‐glucose pyrophosphorylase ( Müller‐Röber et al. 1992).

GC-MS analysis revealed that amongst the metabolites analysed a large fraction (33) were significantly changed in at least one of the transgenic lines. A comparison of the data obtained for the three transgenic lines using GC-MS profiling with the values previously published from HPLC analysis for amino acids and organic acids ( Trethewey et al. 1998; Trethewey et al. 1999) shows that the profiling data are in broad agreement with the previously determined values ( Fig. 3). Our general experience suggests that the differences observed in the extent of quantitative changes are due to the normal differences between the growing conditions of different batches of tubers.

All the scientific conclusions previously drawn about the redistribution of metabolism in the transgenic lines can be drawn from this single profiling analysis. In particular, the increase in glycolysis, amino acids and organic acids in the line expressing the yeast invertase in the cytosol can be clearly deducted from the data in Fig. 3. With respect to the transgenic line expressing a yeast invertase in the apoplast (U‐IN1), it is apparent from Fig. 3 that this line does not have an elevated respiratory flux.

With respect to the line expressing the yeast invertase in the cytosol, the appearance of 6‐phosphogluconic acid is a new result and can be taken as a strong indication of a significantly increased level of the oxidative pentose phosphate pathway, which might be expected given the large increase in glucose‐6‐phosphate in this line.

A most remarkable and unexpected finding concerns the presence of dramatically increased levels of disaccharides such as maltose, isomaltose and trehalose in the line expressing the cytosolic invertase. This result has been found in three independent lines (data not shown) Specifically, the presence of trehalose is an exciting observation. In addition to confirming and extending previous observations on the capacity of higher plants to synthesize trehalose under specific conditions ( Goddijn & Smeekens, 1998; Müller et al. 1999), this finding is of relevance as trehalose has been discussed extensively in the literature as a possible signal for the metabolic status of a given cell. The fact that trehalose appears only in those cells where an increased rate of glycolysis and respiration has been observed makes this a fascinating and stimulating observation. Further comparisons with other lines displaying a change in the rate of glycolysis and respiration must reveal whether or not this exciting but nevertheless simple interpretation is true.

The transgenic lines have been analysed in significant detail in earlier publications by applying conventional techniques such as enzyme assays and HPLC. Nevertheless, the occurrence of trehalose and other disaccharides has not been noted, mainly due to the fact that the methods used previously were not capable of determining these compounds. The unbiased approach for metabolic profiling presented here gives a clear advantage in providing the opportunity to find unexpected events, as exemplified by the identification of trehalose, and thus may provide novel insights into metabolic networks.

Conclusions

Using GC-MS as a system for separation and detection of metabolites, we were able to develop a simple procedure allowing the simultaneous analysis of a large group of compounds representing sugars, sugar alcohols, amino acids, organic acids and some special compounds in plant extracts. This method allows an unbiased study of a range of metabolic pathways with only minimal effort. As this technology utilizes MS, it also combines high sensitivity with high specificity and, as shown here, high reproducibility.

We are presently in the process of applying this technique to a number of other plant species. Depending upon the species and the tissue, between 50 and 400 different compounds can be detected when the analysis is applied to hydrophilic and lipophilic classes of compounds. As this system has the potential to be fully automated, we believe that in the future it will represent a major tool for characterizing the metabolic status of a plant with respect to environmental, developmental or genetic factors.

Experimental procedures

Plant material

Potato plants, Solanum tuberosum L. cv. Desirée, were supplied by Saatzucht Lange (Bad Schwartau, Germany). Plants were grown in a greenhouse at 22°C under a 16 h light/8 h dark regime with supplementary artificial light under a minimum of 250 μmol photons m ² sec −1. In this paper the term 'developing tubers' refers to tubers above 10 g that were harvested from 10‐week‐old plants.

Microtubers were generated in vitro as described by Veramendi et al. (1999a). Single‐node explants were cultured in MS media ( Murashige & Skoog, 1962) containing 6% (w/v) sucrose and 11.6 μm kinetin. Sets of 15 explants were kept in the dark in glass jars containing 50 ml MS medium. Microtubers were harvested after 4 weeks at 20°C.

Chemicals

All chemicals and pure standard substances were purchased from either Sigma-Aldrich Chemie GmbH (Deisenhofen, Germany) or Merck KGaA (Darmstadt, Germany).

Extraction and derivatization of potato tuber metabolites for GC-MS analysis

Tuber slices or whole microtubers were immediately frozen in liquid nitrogen and stored at −70°C until further analysis. A polar metabolite fraction was obtained from either approximately 50 mg microtuber or 100 mg tuber slices by Ultra Thurax T25 (IKA Labortechnik, Staufen, Germany), homogenization in 1400 μl 100% methanol with 50 μl internal standard (2 mg ribitol ml −1 water). The mixture was extracted for 15 min at 70°C. The extract was vigorously mixed with 1 vol water and subsequently centrifuged at 2200 g. Aliquots of the methanol/water supernatant (1.00 or 0.25 ml) were dried in vacuo for 6-16 h. The dried residue was redissolved and derivatized for 90 min at 30°C (in 80 μl of 20 mg ml −1 methoxyamine hydrochloride in pyridine) followed by a 30 min treatment at 37°C (with 80 μl MSTFA). 40 μl of a retention time standard mixture was added prior to trimethylsilylation. This retention time standard mixture contained 3.7% (w) heptanoic acid, 3.7% (w) nonanoic acid, 3.7% (w) undecanoic acid, 3.7% (w) tridecanoic acid, 3.7% (w) pentadecanoic acid, 7.4% (w) nonadecanoic acid, 7.4% (w) tricosanoic acid, 22.2% (w) heptacosanoic acid and 55.5% (w) hentriacontanoic acid dissolved in tetrahydrofuran at 10 mg ml −1 total concentration.

Standard substances for peak identification were dissolved in water at 10 mg ml −1. A 5 μl volume of standard solution was dried in vacuo and derivatized with 50 μl of 20 mg ml −1 methoxyamine hydrochloride in pyridine and 50 μl MSTFA, as described above.

To establish the efficiency of the extraction procedure, the recovery of various standard metabolites was determined by the addition of authentic metabolite standards to the tissue sample at the start of the extraction procedure. Standard substances were added in threefold excess of the determined endogenous concentrations. Estimates of recovery were 138% for alanine, 100% for aspartic acid, 107% for glycine, 107% for isoleucine, 99% for leucine, 92% for lysine, 106% for phenylalanine, 90% for proline, 118% for valine, 127% for serine, 110% for fructose, 78% for fructose‐6‐phosphate, 101% for galactose, 129% for glucose, 78% for glucose‐6‐phosphate, 115% for maltose, 100% for sucrose, 101% for inositol, 108% for mannitol, 139% for glyceric acid, 117% for fumaric acid, 98% for isocitric acid, 118% for malic acid, 99% for shikimic acid and 73% for succinic acid.

GC-MS analysis

Sample volumes of 1 μl were injected with a split ratio of 25 : 1 using a hot‐needle technique. The GC-MS system consisted of an AS 2000 autosampler, a GC 8000 gas chromatograph and a Voyager quadrupole mass spectrometer (ThermoQuest, Manchester, UK). The mass spectrometer was tuned according to the manufacturer's recommendations using tris‐(perfluorobutyl)‐amine (CF43). Gas chromatography was performed on a 30 m SPB‐50 column with 0.25 mm inner diameter and 0.25 μm film thickness (Supelco, Bellfonte, CA, USA). Injection temperature was 230°C, the interface set to 250°C and the ion source adjusted to 200°C. The carrier gas used was helium set at a constant flow rate of 1 ml min −1. The temperature program was 5 min isothermal heating at 70°C, followed by a 5°C min −1 oven temperature ramp to 310°C and a final 1 min heating at 310°C. The system was then temperature equilibrated for 6 min at 70°C prior to injection of the next sample. Mass spectra were recorded at two scans per sec with an m/ z 50-600 scanning range. The chromatograms and mass spectra were evaluated using the masslab program (ThermoQuest, Manchester, UK). A retention time and mass spectral library for automatic peak quantification of metabolite derivatives was implemented within the masslab method format.

Statistical analysis

The t‐tests were performed using the algorithm incorporated into Microsoft excel (Microsoft Corporation, Seattle, WA, USA). The word significant is used in the text when the change in question has been confirmed to be statistically significant ( P < 0.05) with the t ‐test.

Acknowledgements

We would like to thank the gardeners for taking excellent care of the greenhouse plants. Our special thanks are assigned to Dr Alisdair Fernie for proofreading the manuscript and his helpful discussions. We would also like to thank Kristina Zubow for her patient and tireless efforts in creating the mass spectral library.

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Simultaneous analysis of metabolites in potato tuber by gas chromatography-mass spectrometry

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A new method is presented in which gas chromatography coupled to mass spectrometry (GC-MS) allows the quantitative and qualitative detection of more than 150 compounds within a potato tuber, in a highly sensitive and specific manner.

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Dedicated to Dr. med. Claudia R. Maurer, Homburg/Saar, Germany.

Abstract

Gas chromatographic-mass spectrometric (GC-MS) procedures for the systematic toxicological analysis of several categories of drugs relevant to clinical toxicology, forensic toxicology and doping control are reviewed. Papers from 1981 to 1991 are taken into consideration. They describe the detection of acute or chronic intoxication and the detection of drug abuse. Screening procedures are included for the following categories: barbiturates and other sedative-hypnotics, anticonvulsants, benzodiazepines, antidepressants, phenothiazine and butyrophenone neuroleptics, central stimulants (amphetamines, cocaine), hallucinogens (LSD, phencyclidine, tetrahydrocannabinol), opioid (narcotic) and other potent analgesics, non-opioid analgesics, antihistamines (histamine H ₁-receptor blockers), antiparkinsonian drugs, β-blockers (β-adrenoceptor blockers), antiarrhythmics (class I and IV), diuretics, laxatives and their metabolites. Methods for confirmation of results obtained by screening procedures using immunoassay or chromatographic techniques are also included. GC-MS procedures for the simultaneous detection of several categories of drugs, the so-called "general unknown analysis", are reviewed. The toxicological question to be answered and the consequence for the choice of an adequate method, the sample preparation and the chromatography itself are discussed. The basic information about the biosample assayed, work-up, GC column, mass spectral detection mode, reference data and sensitivity of each procedure are summarized in tables, arranged according to the category of drug. Examples of typical GC-MS applications are presented. Fragment ions that are suitable for mass spectral screening for particular categories of drugs and for general unknowns are tabulated.

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Systematic toxicological analysis of drugs and their metabolites by gas chromatography-mass spectrometry

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Dedicated to Dr. med. Claudia R. Maurer, Homburg/Saar, Germany. Gas chromatographic-mass spectrometric (GC-MS) procedures for the systematic toxicological analysis of several categories of drugs relevant to clinical toxicology, forensic toxicology and doping control are reviewed. Papers from 1981 to 1991 are taken into consideration.

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GC–MS analysis of volatile organic compounds from Bambara groundnut rhizobacteria and their antibacterial properties

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GC–MS analysis of volatile organic compounds from Bambara groundnut rhizobacteria and their antibacterial properties

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World Journal of Microbiology and Biotechnology

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Abstract

Bacterial metabolites have been observed to be important in new drug formulation for both plant, animals and human beings. The aim of this study was to identify the different bioactive compounds found in three rhizobacterial isolates ( B. amyloliquefaciens, B. thuringiensis and Bacillus sp.) from the rhizosphere of Bambara groundnut and to assay for their antibacterial properties. Gas chromatography mass spectrometry (GC-MS) was used to carry out the analysis using seven extraction solvents. In the GC-MS analysis, 68 compounds were identified based on peak area percentage, retention time and structure. From the bioactive compounds in B. amyloliquefaciens and B. thuringiensis, the peak area percentage shows that dimethylfuvene from ethyl acetate extraction had the highest relative abundance with 89.11% while Formic acid 2-methylpropyl ester from hexane extraction had the lowest with 6.25%. Others are tridecane, acetic acid butyl ester, paraldehyde, s-(+)-1,2 propanediol, tropone, phthalan and p-xylene with relative abundance of 61.72%, 60.41%, 83.79%, 71.53%, 24.06%, 86.72% and 64.33% respectively. These extracts inhibited the growth of the four test organisms, Bacillus cereus, Pseudomonas aeruginosa, Micrococcus cryophilus and Enterococcus feacalis. Butanol extract from B. amyloliquefaciens had 28 mm zone of inhibition against B. cereus compared to 18 mm and 16 mm by Bacillus sp. and B. thuringiensis respectively. Its zone of inhibition was 24 mm zone against M. cryophilus compared to 12 mm and 19 mm by Bacillus sp. and B. thuringiensis respectively. Butanol extract from B. thuringiensis suppressed E. feacalis and P. aeruginosa having 23 mm and 26 mm zones of inhibition respectively. This was higher compared to Bacillus sp. and B. amyloliquefaciens having 18 mm/15 mm and 21 mm/15 mm against E. feacalis and P. aeruginosa respectively. Hexane and ethyl acetate extract from Bacillus sp. suppressed P. aeruginosa with 12 mm and 17 mm inhibition zones respectively compared to no inhibition zones from hexane extract of B. amyloliquefaciens and B. thuringiensis. Zones of inhibition of 2 mm and 6 mm were observed against P. aeruginosa from ethyl acetate extract of B. amyloliquefaciens and B. thuringiensis respectively. These results suggest that the three isolates are quite rich in the production of bioactive compounds that are also very effective antibacterial agents. These volatile organic compounds are promising compounds for more antibacterial bioactivity development.

Graphical abstract

Introduction

Many rhizobacterial species are involved in plant growth promotion (PGP) and biocontrol activities. Diverse mechanisms are applied directly and or indirectly such as indole acetic acid production and phosphate solubilisation which have been used to achieve this PGP potential (Ajilogba and Babalola 2013). Others are able to suppress or antagonise the growth of competing or pathogenic microbes that can cause disease in or/and around the plant by the production of hydrogen cyanide (HCN), siderophores, antibiotics and other antibacterial compounds such as 2,4-diacetylphloroglucinol and phenazine derivatives (Szentes et al. 2013).

Rhizobacteria are recognized as potential sources of bioactive substances and they are therefore involved in the production of secondary metabolites in the rhizosphere (Kanchiswamy et al. 2015). Rhizobacteria from leguminous crops are important producers of volatile organic compounds (VOCs). As natural nitrogen-fixers, they help trap atmospheric nitrogen and make it available to plants for plant growth and development. Hydrogen cyanide is a volatile compound and an antibiotic effective against other pathogenic organisms and roots of weeds (Ali et al. 2015). It is a common metabolite found in 50% of Bacillus and over 88% of Pseudomonas in the rhizospheric soil and plant root nodules (Ahmad et al. 2008).

Bambara groundnut is important in Agriculture and an extremely drought-tolerant crop (Masindeni 2006). It can perform well and have good crop yield on marginal soils and soils that have undergone water stress compared to other legumes (Brink et al. 2006). It grows well even in poor and infertile soils (Opoku 2010). Also, it can grow on soils of both high and low nitrogen content and high and low temperature conditions (Baudoin and Mergeai 2001). Because it tolerates poor soil, farmers with poor resource especially in funding of fertilizer to increase yield are encouraged to farm more with Bambara groundnut. Report shows that the ability of Bambara groundnut to tolerate poor soil is advantageous against being planted in nitrogen-rich soil. This is because nitrogen-rich soils increase vegetative growth of Bambara groundnut as against crop pod and seed productivity (Baudoin and Mergeai 2001). As a legume, root nodules bacteria form symbiotic association with Bambara groundnut roots. This helps to increase the nitrogen content of the soil in the sense that the bacteria assimilate atmospheric nitrogen, trap it and make it available to the plant in the soil. This process in turn helps to increase soil fertility and crop yield (Masindeni 2006). It has a high tolerance to disease and pest (Ajayi and Lale 2001).

Rhizobia species ( Rhizobium, Bradyrhizobium, Azorhizobium, Allorhizobium, Sinorhizobium and Mesorhizobium) have been known to suppress growth of plant pathogens and also form nodules in symbiotic relationship with legumes (Dakora 2003). The symbiotic relationship also results in the production of nitrogen rich soil. Bambara groundnut was found to form nodules and fix nitrogen in partnership with Bradyrhizobium strain (Laurette et al. 2015). Nodule formation is important in Bambara-microbe interaction; this process starts with production of compounds such as betaines, flavonoids and aldonic acid in the root exudates of the plants. These compounds then have to signal to the rhizobia in a compatible relationship with the compounds. This in turn enhances the production of the nod gene that induces nodulation by interacting with the nodD protein of the cell wall of the rhizobia (Phillips 2000). The rhizobia react to this inducement by producing and releasing the lipo-chito-oligosaccharide Nod factors, which bring about morphological changes in the root hair of the legume. This leads to the formation of an infection thread and development of nodules that finally enhances fixation of nitrogen (Dakora 2003). Some of the molecules found in root exudates/flavonoids of Bambara groundnut include genistein, coumestrol and daidzein (Dakora 2000). The genera involved in secondary metabolite production in the rhizosphere include Pseudomonas, Bacillus, Klebsiella, Azotobacter, Azospirillum, Azomonas, Rhizobium, Bradyrhizobium and Mesorhizobium (Ahemad and Kibret 2014). Secondary metabolites secreted by rhizobacteria are involved in different biological activities such as antimicrobial, antiprotozoal, antihelmintic, antitumor, anticancer, antifungal and immunosuppressant (Graça et al. 2013; Sadrati et al. 2013).

Bacillus spp. have been known to secrete secondary metabolites such as antifungal, antibacterial and siderophores. Antibiotics produced by Bacillus spp. include a wide variety of bacteriocin and have been found to be important as antibiotic precursors, biocontrol agents of plant pathogens and as biopreservatives of beverages and food systems (Sansinenea and Ortiz 2011). An important example of bacteriocin includes megacin secreted by B. megaterium. Khalil et al. ( 2009) observed that bacteriocin was produced and secreted by B. megaterium in both solid and liquid medium and it was able to inhibit the growth of pathogenic bacteria thereby serving as a biopreservative.

Hydrogen cyanide and 2,4-DAPG were observed to function as nematicides and contributed to the toxicity of the bacteria P. fluorescens CHA0 against nematode Caenorhabditis elegans by repelling the nematode (Neidig et al. 2011). Bacilysin, a simple peptide antibiotic was observed to possess both antifungal and antibacterial properties. It is a biocontrol agent against Erwinia amylovora. It was isolated from several strains of B. pumilus, B. subtilis and B. amyloliquefaciens (Arguelles-Arias et al. 2009; Steinborn et al. 2005). Other secondary metabolites have been documented to be secreted by rhizobacteria from different crops for biocontrol and plant growth activities.

Volatile organic compounds (VOCs) are secondary metabolites produced by soil and plant-associated microorganisms, but largely unexplored to date. They are typically small, odorous compounds ( < C15) with low molecular mass ( < 300 Da), high vapor pressure, low boiling point, and a lipophilic moiety. These properties facilitate evaporation and diffusion aboveground and belowground through gas- and water- filled pores in soil and rhizosphere environments (Schulz-Bohm et al. 2017). Also, these bioactive substances and secondary metabolites emitted from the rhizobacteria of Bambara groundnut have not been studied. The main aim of the study was to identify and extract bioactive compounds with the use of diverse solvent and then assay for their antibacterial activities.

Materials and methods

Soil sampling and collection

Soil samples were collected from field trials during the planting period between October 2014 and March 2016 from the North-West University Agricultural Farm, Mafikeng Campus (Lat. 25°78ʹ91ʺ Long. 25°61ʹ84ʺ) Mafikeng, South Africa. Four soil samples were collected randomly from the uprooted Bambara groundnut root rhizosphere at four weeks interval from bulk soil before planting for 16 weeks corresponding to different growth stages of the plant. Twenty four (24) soil samples were collected in all to a depth of 15 cm per sample. Samples were collected in triplicates stored at 4 °C until ready for use.

Preparation of soil samples for bacterial isolation

Soil samples from the rhizosphere of Bambara groundnut were collected. Samples were prepared according to Abdulkadir and Waliyu ( 2012) and placed into sterile 50-ml Falcon tubes (Becton Dickson Paramus, N.J) and kept on ice or at 4 °C until it was needed (within 3 days).

Culturing and isolation of bacteria from samples

Isolation and enumeration of bacteria present in the soil sample were performed by serial dilution plate technique using tryptone soy agar (TSA). A tenfold dilution series was prepared from 10 g of the rhizospheric soil of Bambara groundnut in sterile distilled water and 0.5 ml from the selected dilution was spread plated on the already set tryptic soy agar (TSA). Two (2) loopfuls of each of the bacteria from 3-day old cultures on TSA were each transferred separately to 50 ml tryptic soy broth (TSB) medium and incubated overnight at 28 ± 2 °C. A loopful from each TSB bacterial inoculum was streaked on prepared TSA to have pure isolates from each broth culture.

Molecular identification of selected isolates

Bacterial universal primers (F1R2) amplified 1.5 kb fragment from the genomic DNA of the isolates. Computational analysis was used as a means of identifying the isolates. Analysis of the partial sequences of the 16S rDNA gene of the selected isolates was used as a means of identifying them at the genus level. The BLAST tool was used to compare the partial nucleotide sequences of the 16S rDNA gene of the isolates with the nucleotide database of NCBI web server.

The 16S rDNA gene sequence of the selected isolates was obtained by BLASTn search. Table 1 results show that query sequences were best pairwise aligned with 16S rDNA gene sequence of other firmicutes, proteobacteria and actinobacteria with sequence similarity and identity ranged between 96 and 99%, with E-value of 0.

Table 1 Results of 16S rDNA gene sequence similarities of bacterial isolates and GenBank accession numbers using BLASTn algorithm

Antagonism assay against phytopathogenic fungi and bacteria

Potato dextrose agar (PDA) medium was used as the medium to assay for the antifungal activities of 8 isolates against Fusarium graminearum. This was carried out by inoculating the pathogenic fungi at the centre of the medium and then streaking the isolates on the medium 3 cm away from the fungi. The clear zones between isolates and fungi after incubation for 4 to 7 days at room temperature indicated antagonist interaction between them.

Antagonistic activity of isolates against Gram-negative and Gram-positive bacteria was screened by using perpendicular streak method (Parthasarathi et al. 2010). In perpendicular streak method, Luria Bertani agar (Merck) was used and each plate was streaked with test bacteria isolates at the centre/diameter of the plate and incubated at 30 °C for 48 h to allow optimum growth. Later, 24 h fresh sub-cultured isolated bacteria were prepared and streaked perpendicular to the test isolates and incubated at 37 °C for 24 h. The experiment was carried out in triplicate.

Perpendicular streak method was used to rapidly screen microbes for antagonism (Lertcanawanichakul and Sawangnop 2011).

Cultivation of the isolates for extraction of bioactive compounds

Out of the 8 isolates, 3 Bacillus spp. ( B. amyloliquefaciens, B. thuringiensis and Bacillus sp.) were selected for cultivation based on reviewed literatures on their antagonistic activities. Suitable dilutions (10 ⁻², 10 ⁻⁴, and 10 ⁻⁶) of the isolates were maintained on Luria Bertani (LB) agar medium (Merck) were used to isolate rhizobacteria. All plates (in three replicates) were incubated at 28 °C for 48 h. Each actively growing pure culture of the isolates was used to inoculate 100 ml of Luria Bertani broth (Merck) in a 250-ml Erlenmeyer flask.

Extraction and partial purification of the crude extracts from the rhizobacteria isolates

After 48 h incubation at 30 °C, the LB broth (10%) was used to seed culture in three 500-ml Erlenmeyer flasks each containing 100 ml of fermentation medium (glucose, oatmeal, yeast extract, NaCl, CaCO3 at pH 7.0). The cultures were incubated at 25 °C for 10 days under constant agitation of 220 rpm. The fermentation medium was centrifuged for 20 min at 8000 × g to remove the bacterial cells. The supernatant was shared into 7 equal volumes of 10 ml each depending on the number of solvents used, and then each was extracted with 60 ml of organic solvent. A range of extraction solvents (7) was screened for effectiveness, including petroleum ether, n-hexane, chloroform, ethyl acetate, benzene, ethanol and n-butanol. From the extracts, 1 µl was used for the biological assay.

Biological assay

The different test organisms were spread on plates and holes were bored. Each hole was filled with 1µl of each solvent extracts from the isolates Inhibition zones were measured after 24 h incubation at 37 °C. Agar-well diffusion method was used for the microbial extracts as it is widely used to evaluate the antimicrobial activity of plants or microbial extracts (Magaldi et al. 2004; Valgas et al. 2007).

Gas chromatography-mass spectrometry (GC-MS) analysis

The partially purified active fractions were analyzed by GC-MS. The analyses of the compounds in the active fractions were run on a GC-MS system (Agilent GC: 6890, with a 7683B Autosampler). The fused-silica Rxi-5Sil MS capillary column (30 m 0.25 mm ID, film thickness of 0.25 mm) was directly coupled to an Agilent variant. Oven temperature was programmed (35 °C for 5 min, then 35-300 °C at 10 °C/min) and subsequently, held isothermal for 20 min. The injector port; was 250 °C, the transfer line: 290 °C, splitless. Volume injected: 0.2 ml and the column flow rate was 1 ml/min of 1 mg/ml solution (diluted in chloroform). The peaks of components in gas chromatography were subjected to mass-spectral analysis.

The MS was a LECO Pegasus 4D recording with a EI-source at − 70 eV; the solvent delay was 9 min. Scan time 1.5 s; acquisition rate 10 spectra/second; mass range 50-1000 amu; detector voltage 1800 V, and Ion source temperature: 250 °C. Data were recorded in TIC mode. The software adopted to handle the mass spectra and chromatograms was an Agilent chemstation software. The constituents were identified after comparing with available data in the GC-MS library in the literatures.

Mass spectrometer

The GC-MS mass spectrum data were analyzed using Mnova 11.0.1 and the database of National Institute Standard and Technology (NIST) was used to interpret analyzed data. Comparison of the mass spectrum of the unidentified components released by the bacterial isolates was carried out against the mass spectrum of already known components available in the NIST library. The name, molecular weight and peak area percentage of unknown compounds were evaluated by the software as observed from the chromatogram. The name and structure of the components of the test materials were confirmed.

Results

Cultivation of rhizobacteria isolates

Three rhizobacterial isolates used in the field were BAMhi, BAMli and BAMr. These have been identified as Bacillus strains and they have been deposited in the GenBank (Table 1). BAMhi, BAMli and BAMr were identified as Bacillus amyloliquefaciens, Bacillus thuringiensis and Bacillus sp. respectively.

Antimicrobial bacterial assay of identified Bacillus isolates against phytopathogenic fungi and bacteria

The test organisms used in this assay are B. cereus, E. feacalis and Fusarium graminearium which all cause infections in man, animals and plants with high mortality rates. Antagonism against phytopathogenic fungi and bacteria is observed by isolated rhizobacteria Bacillus amyloliquefaciens, Bacillus thuringiensis and Bacillus sp. (Fig. 1).

Antimicrobial assay of Bacillus amyloliquefaciens (hi), Bacillus thuringiensis (li) and Bacillus sp (r) against Bacillus cereus (BC), E. feacalis (EF) and Fusarium graminearium (fg)

GC-MS analysis carried out on the rhizobacteria isolates

The GC-MS results revealed that from the three identified Bacillus strains, Bacillus amyloliquefaciens, Bacillus thuringiensis and Bacillus sp., 31 VOCs were identified from isolate B. thuringiensis which was highest number of metabolites from the seven different extraction solvents used. B. amyloliquefaciens had the least with 15 VOCs identified while isolate Bacillus sp., had 22 VOCs identified (Table 3). With B. Amyloliquefaciens, the solvent with the highest metabolites is hexane with 5 VOCs while benzene and chloroform were the least with 1 VOC each. With B. thuringiensis, the solvent with the highest VOC was methanol having 15 VOCs while chloroform was the least with only 1. In Bacillus sp., the solvent with the highest VOCs was also hexane with 13 while benzene and chloroform had no hits. This shows that hexane detected the highest number of VOCs (22) from the 3 isolates while chloroform detected the least (2) while a total of 68 VOCs were detected from the isolates (Fig. 2 and Tables 3, 4, 5) in which case none of them has been identified before from the bacterial isolates of Bambara groundnut rhizosphere but some have been identified from rhizobacteria of other plants.

Total number of metabolites detected by GC-MS in B. thuringiensis, B. amyloliquefaciens and Bacillus sp. using seven different extraction solvents

Some VOCs were commonly produced by the three isolates and they include tropone, p-xylene, ethylbenzene, carbazic acid and, isocarbaxazide (5). Others common to B. thuringiensis and B. amyloliquefaciens are formic acid butyl ester, paraldehyde and dimethylfulvene (3). Those common to B. amyloliquefaciens and Bacillus sp. are 2, 2-dimethylhexane-3-one and formic acid 2-methylpropyl ester (2). While those common to B. thuringiensis and Bacillus sp. are fumaronitrile, phenylethylalcohol, benzyl 2-chloroethyl sulfone, N-hydroxylmethylmethyl-2-phenylacetamide, tropeolin, benzene butyl, n-propylbenzene, phthalan, carbobenzoxyhydrazide and 1, 2-dimethylbenzene (10). Only 15 were not common across the 3 isolates and from B. amyloliquefaciens, they are, s-(+)-1, 2 propanediol and 2(3H)-furanone dihydro-5-methyl (2). From B. thuringiensis, they are, 2, 2-dimethylhexanone, 2-oxopropanoic acid, 2, 4-dimethylhexane, dihexylether, tridecylamine, 2, 4-dimethylhexane, cetane and hexane, 1, 1′-oxybis (8). Also from Bacillus sp., they are acetic acid butyl ester, isooctanol, benzene, tridecane and 1, 3-dimethylbenzene (5). (Tables 2, 3, 4).

Table 2 Bioactive compounds identified from GC-MS chromatogram of B. amyloliquefaciens (methanol, benzene, hexane, ethyl acetate, butanol, chloroform extract)

Table 3 Bioactive compounds identified from GC-MS chromatogram of B. thuringiensis (methanol, benzene, hexane, petroleum ether, ethyl acetate, butanol, chloroform extract)

Table 4 Bioactive compounds identified from GC-MS chromatogram of Bacillus sp. (methanol, benzene, hexane, petroleum ether, ethyl acetate, butanol, chloroform extract)

Analysis of GC-MS chromatograms for B. amyloliquefaciens

Benzene and Butanol extract

The GC-MS chromatograms of B. amyloliquefaciens benzene extract reveals a prominent peak which correspond to tropone on comparison with the mass spectra of the NIST library. Its intensity or peak area was 24.06% and 5.45 retention time (RT). The characterized peak and RT of the VOCs from butanol crude extract that is formic acid butyl ester and 2, 2-dimethylhexane-3-one were 19.98%/5.64 and 41.08%/7.72 respectively (Table 2, Figs. 2, 3, 4, 5).

GC-MS chromatogram of Bacillus amyloliquefaciens (BAMhi) comparing extracts using benzene, butanol, chloroform, ethylacetate, methanol and hexane

Chloroform and ethyl acetate extract

The characterized and identified VOCs from chloroform extract that is trichloromethane and that of ethyl acetate extract that is paraldehyde, ethylbenzene, dimethylfuvene and p-xylene had RT corresponding to 5.4, 3.46, 5.05, 5.35 and 6.14 respectively. Dimethylfulvene exhibited the highest intensity or peak area with 89.11% while trichloromethane exhibited the lowest with 27.37% and paraldehyde, ethylbenzene and p-xylene were 83.79%, 52.37% and 47.92% respectively (Table 2, Figs. 2, 3, 6, 7).

Hexane and methanol extract

The GC-MS chromatograms of hexane extract revealed that even though there were 6 peaks, only 4 peaks indicated the presence of secondary VOCs though the 2 peaks without VOCs presence were higher than some of the peaks with VOCs presence (100% and 39.94%). On comparison of the 4 peaks with the mass spectra from NIST library, the highest peak area of 86.72% and RT 3.06 picked 8 VOCs that were characterized and identified as phenylethylalcohol, benzyl 2-chloroethyl sulfone, N-hydroxylmethylmethyl-2-phenylacetamide, tropeolin, benzene, butyl-, n-propylbenezene, phthalan and carbobenzoxyhydrazide. The combination of VOCs at this specific RT and peak area was also noticed with the isolate Bacillus sp. using hexane extract and B. thuringiensis using methanol extract. The other 2 peaks were compared with the mass spectra from NIST library and were characterized and identified as 2(3H)-furanone dihydro-5-methyl and Formic acid 2-methylpropyl ester with the RT of 3.47 and 3.76 respectively and peak area of 9.65% and 6.25% respectively. The 4th peak also had a combination of VOCs ethylbenzene, carbazic acid, isocarboxazid with the RT of 5.05 and peak area of 32.72%. The GC-MS chromatogram of methanol extract showed 3 peaks out of which one of them though with peak area of 100% had no hit. The other 2 peaks indicated presence of VOCs that were compared with the NIST library and where characterized and identified as paraldehyde and s-(+)-1,2 propanediol. Retention time and peak area were highest in s-(+)-1,2 propanediol (3.47 and 71.53) while paraldehyde was 3.28 and 56.22% respectively (Table 2, Figs. 2, 3, 8, 9).

Analysis of GC-MS chromatograms for B. thuringiensis

Benzene and butanol extract

The GC-MS chromatogram of benzene extract from B. thuringiensis did not show very sharp peaks but two of the peaks indicated the presence of VOCs. On comparison to the mass spectra from the NIST library, the compounds were characterized and identified as fumaronitrile and tropone (Figs. 4, 10) with RT of 4.37 and 5.34 respectively. While fumaronitrile had the highest peak area of 35.44% compared to tropone with 25.23%. Two peaks of GC-MS chromatograms of butanol extract from isolate B. thuringiensis were characterized and identified by comparing with the NIST library as formic acid butyl ester and 2, 2-dimethylhexanone (Fig. 5) in the RT of 5.36 and 7.70 respectively. The highest peak area of 43.70% was exhibited by 2, 2-dimethylhexanone while formic acid butyl ester showed the lowest peak area of 15.88% (Table 3, Figs. 4, 5, 10).

Chloroform and ethyl acetate extract

The GC-MS chromatogram of chloroform extract revealed only 1 peak which was characterized and identified as bromodichloromethane (Figs. 6, 10) from the NIST library. The RT and peak area were observed as 4.25, 43.38% respectively. The characterized and identified VOCs from ethyl acetate extract of B. thuringiensis which were observed as 5 peaks when compared with NIST library. The mass spectra corresponded with paraldehyde, 2-oxopropanoic acid, ethylbenzene, dimethylfulvene, p-xylene (Fig. 7) at RT 3.46, 4.45, 5.05, 5.35 and 6.14 respectively. The compound dimethylfulvene had the highest peak area of 89.11% while 2-oxopropanoic acid had the lowest at 41.55% (Table 3, Figs. 6, 7, 10).

Hexane and methanol extract

The characterized and identified VOCs from hexane extract of B. thuringiensis were observed as 3 major peaks (Figs. 8, 9, 10). When compared with NIST library, the mass spectra of the first peak corresponded to ethyl benzene (Fig. 10) in RT of 5.03 and peak area of 26.98%. The second peak corresponded to tridecylamine (Fig. 10) with 3.57 RT and peak area of 12.76%. The third peak was identified as combination of 2, 4-dimethylhexane and dihexylether (Fig. 10) in RT of 3.47 and peak area of 55.54%. GC-MS chromatogram of methanol extract showed 5 peaks with the first peak having a combination of VOCs as was mentioned with B. amyloliquefaciens, hexane extract (Figs. 8, 9, 10). The second peak also had a combination of 2 peaks identified as cetane and hexane, 1, 1′-oxybis and 3.85 in RT and peak area of 7.36%. The third peak was identified as a combination of ethylbenzene, isocarboxazid and carbazic acid with RT in 5.04 and peak area of 32.23%. The fourth peak corresponded to p-xylene in 5.34 RT and had the highest peak area of 100%. The fifth metabolite identified was 1, 2-dimethybenzene in 6.14 RT and 40.58% peak area (Table 3, Figs. 8, 9, 10).

GC-MS chromatogram of Bacillus thuringiensis (BAMli) comparing extracts of benzene, butanol, chloroform, ethyl acetate, hexane, methanol and petroleum ether extract

Petroleum ether extract

GC-MS chromatogram of petroleum ether extract showed 4 peaks with 2 peaks characterized and the metabolites identified as 2,4-Dimethylhexane and Ethylbenzene in RT 3.47 and 5.03 respectively. The highest peak area of 56.58% was exhibited by 2,4-Dimethylhexane while Ethylbenzene exhibited 25.79% peak area (Table 3, Figs. 9, 10).

Analysis of GC-MS chromatograms for Bacillus sp.

Butanol and ethyl acetate extract

The GC-MS chromatogram of butanol extract of isolate Bacillus sp. shows 2 sharp peaks, indicating the presence of VOCs characterized and identified as acetic acid, butyl ester and 2,2-dimethyl-3-hexanone (Table 4, Figs. 2, 5, 11) in RT 3.84 and 7.69 respectively. Acetic acid, butyl ester has the highest peak area of 60.41% while 2,2-dimethyl-3-hexanone has a peak area of 47.28%. The GC-MS chromatogram of ethyl acetate shows 2 peaks of characterized and identified VOCs p-xylene and 1,2-dimethylbenzene (Fig. 11) in RT of 5.35 and 6.15 respectively. The highest peak area of 64.33% was observed in p-xylene while1,2-dimethylbenzene had a peak area of 36.89% (Table 4, Figs. 2, 7, 11).

GC-MS chromatogram of Bacillus spp. (BAMr) comparing extracts using butanol, ethyl acetate, hexane, petroleum ether and methanol

Hexane extract

Hexane extract produced GC-MS chromatograms of 4 peaks indicating the presence of microbial VOCs. The first peak had combination of VOCs as was mentioned in B. amyloliquefaciens, hexane extract and B. thuringiensis methanol extract in RT of 3.06 and the highest peak area of 86.72%. The third peak was identified as a combination of ethylbenzene, isocarboxazid and carbazic acid which was the same with the third peak of methanol extract of B. thuringiensis (Fig. 8) with RT in 5.05 and peak area of 32.23%. The remaining 2 peaks were characterized and identified as isooctanol and formic acid, 2-methylpropyl ester in RT 3.47 and 3.76 respectively. Isooctanol showed the lowest peak area of 7.46%, while formic acid, 2-methylpropyl ester also showed a low peak area of 8.78% (Table 4, Figs. 2, 8, 11).

Methanol and petroleum ether extract

The GC-MS chromatogram of petroleum ether extract showed 2 peaks indicating the presence of microbial VOCs. In comparison with the mass spectra of VOCs in the NIST library, the compounds were characterized and identified as tridecane and 1,3-dimethylbenzene (Figs. 9, Fig. 11) in RT of 3.48 and 5.34 respectively. Tridecane showed the highest peak area of 61.72% while 1,3-dimethylbenzene showed peak area value of 52.57%. Chromatogram from methanol extract only picked one peak indicating the presence of microbial VOCs which were characterized and identified as fumaronitrile, tropone and benzene (Figs. 9, 11) in RT 3.81 and peak area of 34.22% (Table 4, Figs. 2, 9, 11).

Antibacterial potential of bioactive compounds/volatile organic compounds (VOCs)

The organisms used in this assay are B. cereus, P. aeruginosa, E. feacalis and M. cryophilus which are locally isolated organisms (Sansinenea and Ortiz). B. cereus is a food poisoning pathogen worldwide with serious health implication, so it is very important in the food industry and agriculture (Tewari and Abdullah 2015). E. feacalis and P. aeruginosa are among the diverse type of bacteria capable of causing infection in man, animals and plants causing plants death 7 days post inoculation (Jha et al. 2005; Walker et al. 2004). M. cryophilus is a psychrophilic bacterium that is able to regulate lipid acyl chain length. (Kates 2012), this can also make it very resistant to biomolecules. The bacteria used for the tests were resistant to at least one known antimicrobial agent (Adegboye and Babalola 2013). Antagonistic assay against selected bacterial pathogens was carried out on the crude extracts of VOCs reveals that butanol extracts showed more antagonism against the selected pathogens (Fig. 12).

Antibacterial activities of crude extracts ( BU Butanol, PE Petroleum ether, EA ethyl acetate, HE Hexane) of Bacillus amyloliquefaciens (hi), Bacillus thuringiensis (li) and Bacillus spp (r) against selected pathogens B. cereus (BC), M. cryophilus (MC), P.aeruginosa (PA) and E. feacalis (EF)

Butanol extract from B. amyloliquefaciens had 28 mm zone of inhibition against B. cereus compared to 18 mm and 16 mm by Bacillus sp. and B. thuringiensis respectively. It was also the highest with 24 mm zone of inhibition against M. cryophilus compared to 12 mm and 19 mm by Bacillus sp. and B. thuringiensis respectively. Butanol extract from B. thuringiensis was highly effective against E. feacalis and P. aeruginosa having 23 mm and 26 mm zones of inhibition respectively. This was also the highest compared to zones of inhibition from Bacillus sp. and B. amyloliquefaciens having 18 mm/15 mm and 21 mm/15 mm against E. feacalis and P. aeruginosa respectively. The hexane and ethyl acetate extract from Bacillus sp. were effective against P. aeruginosa with 12 mm and 17 mm zone of inhibition respectively compared to no zones of inhibition from hexane extract of B. amyloliquefaciens and B. thuringiensis. Zones of inhibition of 2 mm and 6 mm were observed against P. aeruginosa from ethyl acetate extract of B. amyloliquefaciens and B. thuringiensis respectively. Petroleum ether extract from bioactive compounds of isolate B. amyloliquefaciens had the least antagonism activity against test organisms, while the butanol extract of bioactive compounds from B. amyloliquefaciens had the highest antagonism activity against all test organisms (Table 5).

Table 5 Antibacterial activities of the active fractions against test organisms

The relevance of these data to the production of bioactive molecules by microbial cultures

Even though hexane had the highest number of bioactive metabolites produced, its antibacterial activities were not as inhibitory against test organisms as compared to that of butanol extract with only 6 different metabolites produced. It is also worthy of note that even though some of the solvents showed metabolites production in chloroform, methanol and petroleum ether, there were no antibacterial activities from their fractions based on zones of inhibition (Tables 2, 3, 4, Fig. 12). The relative abundance of the various bioactive compounds depicted by the peak area percentage revealed that dimethylfuvene from ethyl acetate extraction had the highest relative abundance with 89.11% while Formic acid 2-methylpropyl ester from hexane extraction had the lowest with 6.25%. Among the bioactive compounds produced by the three selected isolates were tridecane, acetic acid butyl ester, paraldehyde, s-(+)-1,2 propanediol, tropone, phthalan and p-xylene with relative abundance of 61.72, 60.41, 83.79, 71.53, 24.06, 86.72 and 64.33 respectively.

Discussion

Rhizobacteria have been observed to produce secondary metabolites especially antibiotics (Raaijmakers and Mazzola 2012). Gas Chromatography-Mass Spectrometry (GC-MS) analysis of crude extract from the 3 isolates in this study detected 68 compounds using 7 extraction solvents while Streptomyces spp. isolated from the rhizosphere of chili pepper was observed to detect 77 compounds using ethyl acetate and based on RT and peak area percentage (Jalaluldeen et al. 2015). A total of 22 volatile compounds were secreted by 6 bacterial strains from the rhizosphere of lemon plant (Gutiérrez-Luna et al. 2010) while a total of 38 volatiles were released by 4 PGPR strains and were used to trigger growth in Arabidopsis thaliana (Farag et al. 2006). This difference in bioactive compounds detected could be as a result of the volatility of the compounds or extraction solvent (Groenhagen et al. 2013). Rhizobacterial strains very close to B. thuringiensis, B. amyloliquefaciens and Bacillus sp. have been involved in the production of one or more bioactive compounds (Arguelles-Arias et al. 2009; Bacon et al. 2015; Baysal et al. 2008). Bacillus spp. are able to withstand harsh environmental conditions in the soil because of the production of spores that are able to withstand extreme weather conditions. They are also able to compete favourably with other microbes as a result of VOCs production (Amin et al. 2015). Bacillus spp. have been observed to be able to produce diverse VOCs which are effective against human beings, plant and animal pathogens (Kai et al. 2007).

Antagonistic activities have been reported from many origins including bacteriocins, enzymes and volatiles while the effect of volatiles have been understudied but not given the relevant attention it deserves. In this study, it was shown that the rhizosphere of Bambara groundnut was able to attract organisms which could be important pesticide-producing agents as well as useful in drug production and other industries. The GC-MS result from the three isolates showed that mostly the same compound was present and this could be attributed to the three organisms being members of the same genus.

According to Gao et al. ( 2018), benzothiazole and 4-di-tert-butylthiophenol were key inhibitory VOCs produced by B. subtilis CF-3. They were able to significantly inhibit the growth of postharvent fungi Monilinia fructicola and Colletotrichum gloeosporioide with peak of 73.46% and 63.63% respectively. This peak is close to s-( +)-1,2 propanediol produced by B. amyloliqefcaiens with peak 71.53.

Eight Bacillus strains showed the presence of ketones which included 3-methyl-2-pentanone, 2-heptanone, 2-octanone, 2-decanone, 5-methyl-2-hexanone, 2-nonanone, 2-dodecanone, 2-undecanone and 5-methyl-2-heptanoneand2-pentanone. They are rich resources of bioactive volatiles and were found to be effective in inhibiting growth of Fusarium solani (Li et al. 2015).

Four Bacillus amyloliquefaciens subsp. Plantarum strains UCMB5033, UCMB5036, UCMB5113 and FZB42 were inoculated with fungal strains Botrytis cinerea 30,158, Alternaria brassicicola 20,297, A. brassicae 980:3, Sclerotinia sclerotiorum 13 MM and Verticillum longisporum D11 which are phytopathogens. Some of the VOCs produced were 2,3butanedione, acetoin, 5-methyl-heptanone, 2-methylpyridine, 2-pentanone, 2-heptanone and 3-methylbutanol. The study revealed that Bacillus VOCs anatagonised several fungal growth at varied degrees (Asari et al. 2016).

It was observed that Fusarium oxysporum f. sp. Cucumerinum J. H. Owen (FOC)-infected cucumber seedlings secreted more citric acid and fumaric acid, which stimulated the chemotaxis response and biofilm formation of B. Amyloliquefaciens SQR9, and thus enhanced the root colonization of B. Amyloliquefaciens SQR9 and suppressed disease infection (Liu et al. 2014).

Bacillus subtilis FA26 was evaluated for its antibacterial activities against Clavibacter michiganesis ssp. Sepedonicus (Cms), the causal agent of bacterial ring rot of potato. It was observed that B. subtilis FA26 produced 11 VOCs with 4 of them significantly active against Cms (benzaldehyde, nonanal, benzothiazole and acetophenone) (Rajer et al. 2017).

From the 36 volatile compounds detected from Bacillus amyloliquefaciens NJN 6, 11 produced volatile compounds (VOCs) that inhibit the growth and spore germination of Fusarium oxysporum f.sp. Cubense completely. They included 2-undecanone,2-dodecanone,and2-tridecanone, benzothiazoles phenol and 2,3,6-trimethylphenol (Yuan et al. 2012).

Bacillus atrophaeus CAB-1 was able to suppress cucumber powdery mildew and tomato gray mold activity using lipopeptides, a novel protein and volatile compounds. The volatile compounds produced by CAB-1 were complex, including a range of alcohols, phenols, amines, and alkane amides. O-anisaldehyde represented one of the most abundant volatiles with the highest inhibition on the mycelial growth of B. cinerea (Zhang et al. 2013).

Contained in a number of pesticides and antidepressants are notable compounds produced such as 1,3-dihydroisobenzofurans, commonly known as Phthalan (extracted also in this study). It is comprised of a large number of bioactive compounds in which some of them are antihistaminic, antifungal, antisecretory and antidepressive (Karmarkar et al. 2014). This compound has been synthesized from plants and other compounds and have been commercialised also but so far not from rhizobacteria as documented in this study.

It was observed that the bioactive compounds extracted using butanol were the potent antibacterial compounds from the isolates. They showed considerably larger zone of inhibition compared to other selected solvent extracts while the least zones were recorded in the petroleum extract solvents (Table 3). One solvent performs better than the other because of the difference in polarity. Butanol extract may show antagonism against some selected pathogens because it has four carbon atoms in its structure and allows to penetrate into the cell walls compared with ethanol that has two carbon atoms and the other solvents. Antimicrobial activities increase as the polarity of the solvents increase (Parekh and Chanda 2007). Also, according Bakht et al. ( 2011), butanol extract also had the highest activity against bacterial pathogen Erwinia carotovora.

The GC-MS result is in the order hexane > methanol > ethyl acetate > butanol > benzene and petroleum ether while chloroform < petroleum ether (Figs. 2, 3, 4, 5, 6). Benzene is less than petroleum ether while chloroform had the least number of notable compounds.

Some of the bioactive compounds like tropolone have been shown to have a strong inhibitory activity on plant growth and are known to have antimicrobial and insecticidal activity. Troplone are found in Cupressaceae trees, such as Chamaecyparis obtusa, Thuja plicata, Thujopsis dolabrata var. Hondai, Hiba arboruitae, Cupressus lusitanica. It reduced the production of ethylene in peach seeds that were excised and also inhibited ethylene production by suppressing the production of 1-aminocyclopropane-1-carboxylate (ACC) synthase and oxidase in the mesocarp of wounded winter squash. Strong antifungal activity and broad antimicrobial spectrum of tropolone have led to their wide utilization in agriculture, clinical products, cosmetics and other areas (Saniewski et al. 2007). They are able to chelate iron and make it available for plant growth promotion (Saniewski et al. 2007). Another compound, p-xylene which is a chemical feedstock in the chemical industry was secreted by the three selected isolates, these were all Bacillus spp. Other bioactive compounds were tridecane, fumaronitrile, isocarboxazid and tridecylamine.

The data from the different solvents used was able to clarify the fact that more bioactive compound can be synthesized from rhizobacteria if the appropriate solvents are used. In this study, Hexane and Butanol where able to extract more bioactive compounds compared to the other solvents used. But Butanol extracts had the highest antagonistic activity against test organisms. The butanol extract from the 3 bacillus isolates were more effective antimicrobials compared with the other extracts. The compounds in the extracts includes 2, 2-dimethylhexanone, acetic acid,butylester, 2,2-dimethyl-3-hexanone, Formic acid butyl ester and 2,2-dimethylhexane-3-one. The study by Hossain et al. ( 2013), revealed that 2, 2,-dimethylhexane-3-one was part of the bioactive compounds from the chloroform crude extract of the medicinal plant Datura metel, though its antibacterial properties was not within the scope of the study. This data opens door to more research on application of the use of hexane, ethyl acetate and butanol as a more effect solvent for extraction of more bioactive compounds and also more antagonistic activities respectively. The data presented in this study reveals that there are untapped bioactive compounds in soil microorganisms in the rhizosphere of legumes and in particular Bambara groundnut.

Conclusion

Apart from antibiotics, VOCs are another class of secondary metabolites that have showed great promise in combating pathogenic organisms. The VOCs crude extracts from this study were antagonistic against test organisms, thus, making them good antibacterial compounds for the agricultural, medical and pharmaceutical industries. These three isolates ( Bacillus sp., B. thuringiensis and B. amyloliquefaciens) are good sources of bioactive compounds and the compounds can be modelled into antibacterial agents. Since not much work has been done to exploit these compounds, it means basic research should be conducted more to exploit the hidden potentials of these compounds and the organisms producing them as this might lead to the discovery of new world of metabolism in bacteria. This is going to be the next stage of this research in which case data from this study will be used to determine which compounds are most likely to be responsible for antimicrobial activity in the data presented here, and those most promising antimicrobial compounds can be the subject of future work to prove their antimicrobial activity and to optimize production.

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Acknowledgements

We gratefully acknowledge the North-West University for bursary to the first author and the National Research Foundation, South Africa, for Grant that supports work in our laboratory.

Funding

OOB is deeply appreciative of the 7 years of National Research Foundation (NRF) incentive funding (UID81192).

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Affiliations

Food Security and Safety Niche Area, Faculty of Natural and Agricultural Science, North-West University, Mmabatho, Mafikeng, 2735, South Africa

Caroline F. Ajilogba & Olubukola O. Babalola

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Correspondence to Olubukola O. Babalola.

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# data[].attributes.attachment_link.title
GC-MS analysis of volatile organic compounds from Bambara groundnut rhizobacteria and their antibacterial properties

# data[].attributes.attachment_link.description
Bacterial metabolites have been observed to be important in new drug formulation for both plant, animals and human beings. The aim of this study was to identify the different bioactive compounds found in three rhizobacterial isolates (B. amyloliquefaciens, B. thuringiensis and Bacillus sp.) from the rhizosphere of Bambara groundnut and to assay for their antibacterial properties.

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Gas Chromatography Mass Spectrometry or simply GC-MS is an analysis and separation technique used for smaller and volatile molecules such as benzenes, alcohols and aromatics, and simple molecules such as steroids, fatty acids, and hormones. It involves two techniques combined in a single method: chromatography and spectrometry, hence the name (Thermo Fisher Scientific, n.d.).

# data[].attributes.attachment_link.readable_text
GC/MS begins with the gas chromatograph, where the sample is volatized. This effectively vaporizes the sample (the gas phase) and separates its various components using a capillary column packed with a stationary (solid) phase. The compounds are propelled by an inert carrier gas such as argon, helium or nitrogen. As the components become separated, they elute from the column at different times, which is generally referred to as their retention times.

Once the components leave the GC column, they are ionized by the mass spectrometer using electron or chemical ionization sources. Ionized molecules are then accelerated through the instrument's mass analyzer, which quite often is a quadrupole or ion trap. It is here that ions are separated based on their different mass-to-charge ( m/z) ratios.

The final steps of the process involve ion detection and analysis, with compound peaks appearing as a function of their m/z ratios. Peak heights, meanwhile, are proportional to the quantity of the corresponding compound. A complex sample will produce several different peaks, and the final readout will be a mass spectrum. Using computer libraries of mass spectra for different compounds, researchers can identify and quantitate unknown compounds and analytes.

GC is the separation technique of choice for smaller and volatile molecules such as benzenes, alcohols and aromatics, and simple molecules such as steroids, fatty acids and hormones. It is widely used for chemical analysis, and especially for drug and environmental contamination testing. When combined with MS, GC/MS can be used in both full scan MS or select ion monitoring (SIM) mode to cover either a wide range of m/z ratios or to gather data for specific masses of interest, respectively.

In this section, you will:

Learn which mass spectrometry instruments and technologies are best suited to sample analysis by GC/MS.

Understand how GC/MS helps analyze volatile and small molecules.

Learn about workflows specifically designed for samples undergoing analysis by GC/MS.

GC/MS features

GC/MS information subtopics

# data[].attributes.attachment_link.title
Gas Chromatography Mass Spectrometry (GC-MS) Information | Thermo Fisher Scientific - US

# data[].attributes.attachment_link.description
GC/MS begins with the gas chromatograph, where the sample is volatized. This effectively vaporizes the sample (the gas phase) and separates its various components using a capillary column packed with a stationary (solid) phase. The compounds are propelled by an inert carrier gas such as argon, helium or nitrogen.

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Decision tree supported substructure prediction of metabolites from GC-MS profiles

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Decision tree supported substructure prediction of metabolites from GC-MS profiles

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Abstract

Gas chromatography coupled to mass spectrometry (GC-MS) is one of the most widespread routine technologies applied to the large scale screening and discovery of novel metabolic biomarkers. However, currently the majority of mass spectral tags (MSTs) remains unidentified due to the lack of authenticated pure reference substances required for compound identification by GC-MS. Here, we accessed the information on reference compounds stored in the Golm Metabolome Database (GMD) to apply supervised machine learning approaches to the classification and identification of unidentified MSTs without relying on library searches. Non-annotated MSTs with mass spectral and retention index (RI) information together with data of already identified metabolites and reference substances have been archived in the GMD. Structural feature extraction was applied to sub-divide the metabolite space contained in the GMD and to define the prediction target classes. Decision tree (DT)-based prediction of the most frequent substructures based on mass spectral features and RI information is demonstrated to result in highly sensitive and specific detections of sub-structures contained in the compounds. The underlying set of DTs can be inspected by the user and are made available for batch processing via SOAP (Simple Object Access Protocol)-based web services. The GMD mass spectral library with the integrated DTs is freely accessible for non-commercial use at http://gmd.mpimp-golm.mpg.de/. All matching and structure search functionalities are available as SOAP-based web services. A XML + HTTP interface, which follows Representational State Transfer (REST) principles, facilitates read-only access to data base entities.

Introduction

The identification of the high number of as of yet unidentified metabolic components from GC-MS profiling experiments poses a major challenge in metabolite profiling. Two factors contribute to the high complexity of typical GC-(TOF)-MS experiments. Firstly, as GC-MS inherently requires volatile analytes, metabolites of interest need to be chemically modified, for example by methoxyamination and silylation reagents (Kopka 2006, Lisec et al. 2006). Thus, more than one single analyte per metabolite may be generated and thorough chemical interpretations of observed analytes with respect to their mass spectral and retention index (RI) properties are required. Secondly, a compound library comparison as the most straightforward approach for identifying components from GC-MS analyses relies on the availability of authenticated pure reference substances. Currently, metabolite identification is only possible by a time consuming, manually supervised matching of both the RI information and the reference mass spectra stored in dedicated libraries such as the Golm Metabolome Database (GMD) (Kopka et al. 2005, Schauer et al. 2005), Therefore, the lack of chemically synthesized reference substances and of purified bio-molecules may be seen as the current bottleneck of comprehensive compound identification as identification is only possible if the detected compound is present in the library of references compounds.

In order to provisionally accommodate unidentified compounds, the GMD archives respective Mass Spectral Tags (MSTs). MSTs are defined to represent the combination of chemo-physical properties, namely the mass fragmentation pattern linked to the chromatographic RI information (Wagner et al. 2003). In addition, the GMD also comprises a large compendium of identified compound entries. These entries represent known metabolite structures and are linked to the source (vendor etc.) information of the respective reference substances. Thus, the GMD may represent an ideal resource for the application of supervised machine learning algorithms for compound classification as a means for an automated annotation of MSTs. The GMD compendium may thus be used to enhance the chemical identification process of novel metabolic components discovered by GC-(TOF)-MS based metabolomic screening studies.

Currently, most novel GC-MS based analytical signals remain unidentified as there is no reference substance available. Of the total of up to 1,000 MSTs observed in typical studies, only 50-150 metabolites can be identified. The determination of the chemical sum formula associated with molecule peak and electron-impact induced fragment peaks may be feasible. The unambiguous mass spectral interpretation, however, is in many cases only possible for small molecules bearing a single functional group (Varmuza and Werther 1996). For this reason, hit list based mass spectra similarity matching has evolved as a highly successful tool for the routine assessment of mass spectra (Halket et al. 2005), and large commercial mass spectral libraries, such as NIST08 ( http://chemdata.nist.gov/mass-spc/Srch_v1.7/index.html) have been developed. The employed matching approaches use various similarity scores, which were developed in conjunction with specifics of the gas chromatographic and mass spectrometric technology (Crawford and Morrison 1968, Stein 1999, Stein and Scott 1994). However, a reliable automation of mass spectral matching has not been accomplished so far. Tools utilising RI information for the matching process adapted for the processing of large metabolite profiling experiments, such as TagFinder (Luedemann et al. 2008), recommend manually supervised compound identification.

Beyond similarity-based matching of mass fragmentation patterns, algorithmic approaches that relate mass spectral features to structural properties have been pursued. For example, several low resolution mass spectral classifiers have been reported to yield promising results. Werther et al. ( 1994) successfully applied diverse computational classification techniques to the recognition of simple structural moieties. These authors tested the prediction of 10 structural characteristics and found neural networks to be superior compared to k-nearest neighbour (KNN) classification, linear discriminant analysis, or principal component models. Furthermore, Varmuza and Werther ( 1996) presented an enhanced approach based on random sampling of training mass spectra according to predefined spectral features and the subsequent application of multivariate classification methods or neural networks. Approximately 160 spectral classifiers were developed that cover a significant portion of organic chemistry, but they may only be partially applicable to bio-molecules, such as the primary and secondary metabolites. These spectral classifiers are now part of the NIST software. Since version 2.0, the software package Mass Frontier™ supports the classification of mass spectra ( http://www.highchem.com/new-features/), utilizing three classification methods: Principal Component Analysis (PCA), Fuzzy Clustering, and Self-Organizing Maps (SOM) ( http://www.highchem.com/mass-spectra-classification/) (Steiner et al. 2002). Further enhanced approaches attempt to include isomer prediction from given sum formulas (Varmuza 2001).

Progress has also been made for the prediction of the presence or absence of substructures based on mass spectral features. Mass spectral classifiers were implemented using linear discriminant analysis, LDA, and partial least squares discriminant analysis, PLS-DA (Yoshida et al. 2001), or by selection of characteristic mass fragment combinations (Tang et al. 2003). In view of these successful approaches, we developed a decision tree (DT) based classifier and a web-based interface dedicated to the specific metabolomic needs of GC-MS-based profiling. In contrast to prior general organic chemistry-based efforts, we focus on metabolites and substructures of metabolic origin. Furthermore, we combine the chromatographic RI information (Strehmel et al. 2008) with mass spectral features for classification and substructure prediction. Most importantly, we chose the DT algorithm to solve the classification problem. This algorithm is employed for the recognition of patterns in mass fragmentation spectra that distinguish classes of compounds which either contain or lack a specific predefined chemical moiety. The DTs are made available via the augmented web-interface of the GMD as well as web-services to assist in the annotation of metabolomics data sets.

Methods

Integration of metabolite structures into the GMD

The GMD uses a Microsoft SQL Server 2008™ as the relational database backend for relating the mass spectrum and retention behaviour to an analyte, i.e. the chemically modified compound, which is mapped to represent a metabolite (Fig. 1) (Hummel et al. 2008). Both analyte and metabolite have the properties of a chemical compound and are linked to structures archived as .mol-files and InChI™ codes ( http://www.iupac.org/inchi/). A typical metabolite has one to two analytes, which are generated by the chemical derivatization process inherent to the GC-MS profiling technique. Each analyte has multiple technological versions of MSTs. These replicate mass spectra and RIs are empirically determined using different mass spectral technologies, e.g. time of flight, quadrupole or ion trap based mass detectors, and variations of gas chromatographic systems (Strehmel et al. 2008).

Excerpt of the GMD scheme. MSTs (mass spectral tags, i.e. repeatedly observed mass spectra with retention behaviour) are linked to analytes via experiments and a supervised annotation process. Likewise, analytes are mapped to metabolites. Structural information has been added to both types of compounds, the metabolites and their respective analytes

In the current GMD release, 6,187 mass spectra are available representing 2,444 analytes and 1,535 metabolites. It should be noted that the GMD compendium is biased towards GC-MS accessible, stable, primary metabolites. Therefore, the structural moieties of the metabolite classes, amino acids, organic acids, fatty acids, fatty alcohols, sugars, sugar alcohols and respective conjugates dominate. Structural annotations are in most cases stereo-chemically correct, even though routine GC-MS profiling (Lisec et al. 2006, Wagner et al. 2003) allows only the differentiation of anomeric, epimeric structures and E/Z-geometric isomers.

Decision tree supported substructure prediction

A supervised machine learning approach using a pattern recognition algorithm was chosen to infer correlations between the sub-structure properties of known compounds and the properties of respective MSTs. For every considered functional group, we classify MSTs to belong to either the functional group containing or non-containing groups. Thus, we perform a binary classification. The DT method was applied, because multiple parameter types, categorical and numerical, can be integrated and no assumptions about numerical parameter distributions and about the nature of discriminating functions, e.g. linear, non-linear, multimodal, are required. Secondly, in contrast to the NIST Mass Spectrum Interpreter software for substructure analysis, rules comprised of single feature decisions are returned, which describe the criteria of the mass spectral classification process and are suitable for interpretation by a GC-MS expert.

The checkmol program (Feldman et al. 2005) was executed to automatically extract the 21 most abundant structural features, e.g. substructures or chemical moieties, from the metabolite structures of the GMD (>3% occurrence). Subsequently, we created DTs using mass spectral and retention properties as predictors of the structural features.

The mass spectra used to train the DT algorithm were electron impact spectra of methoxyaminated and trimethylsilylated reference compounds with known structures, natural isotopomer composition, and documented reference compound sources.

It should be noted that-in the current release-we use the MSTs of chemically derivatized metabolites for the analysis of structural features present in non-derivatized metabolite structures. As the DT algorithm supports this approach, we reasoned that the biologist and GMD user's interest lies more on the metabolite structure rather than the methoxyaminated and trimethylsilylated compounds inherently required for GC-MS based metabolite profiling.

MST subsetting and RI assignment

DT training was performed separately for each considered structural feature. For this procedure, the mass spectral compendium of the GMD was divided into those mass spectra associated with metabolites containing the respective structural feature and those in which the structural feature was absent. DT training was performed with and without using the RI information linked to each MST. In order to use the RI information, a subset of training data with empirically determined RIs was created. The supported RI models are based on standardization by 9 n-alkanes (C10-C36) and either a 5%-phenyl-95%-dimethylpolysiloxane capillary column, in short VAR5, or a 35%-phenyl-65%-dimethylpolysiloxane column ( MDN35, Lisec et al. 2006). RI information of 8 variant VAR5 chromatography methods was converted according to Strehmel et al. ( 2008).

Mass spectral features

A mass spectrum can be considered a point in an n-dimensional mass space with n representing the number of individual masses/charge fragment ratios as coordinate axes with associated values corresponding to fragment abundances. In order to characterise mass spectra in terms of those properties, which are potentially relevant for the structural distinction of chemical substance classes, additional spectral features have been proposed, e.g. weighted abundance of single masses, intensities of single masses normalised to the local ion current, averaged intensities of mass intervals, logarithmic transformations, modulo-14 summation, autocorrelation properties, so-called spectrum type features, and characteristic peak series features (Varmuza 2001, Xu et al. 2003).

As DT methods allow the combined use of diverse properties, we extracted three types of spectral features in addition to the above mentioned RI information. (1) Logarithmic intensities of nominal masses, as proposed previously (cf. above), were used in the mass range m/z 70-600. However, only 525 spectral features (" intensity-lg") were allowed after exclusion of ubiquitous mass fragments typically generated from compounds carrying a trimethylsilyl-moiety, namely the fragments at m/z 73, 74, 75, 147, 148, and 149. (2) For an improved feature construction with better discriminative potential (Kotsiantis et al. 2006), we implemented the full set of logarithmic pair-wise intensity ratios, thus, adding 137,550 spectral features to the DT training input space (" ratio lg"). (3) In addition to these features, we used a mass distance measure to represent the mass losses of typical electron impact induced fragmentation reactions. Mass distances caused by the naturally occurring ¹³C-isotopic patterns were excluded. This spectral processing provided 524 additional spectral features (" peak distance") per MST. In detail, only those mass fragments associated with a local intensity maximum were considered for peak distance calculation, whereas flanking mass fragments with descending intensities at ( m/z) − 1(2, 3, ...) or ( m/z) + 1(2, 3, ...) were removed before calculating the peak distance matrix.

In summary, MSTs were pre-processed to obtain those spectral features best representing the probability that a specific fragment is generated from a given compound ( intensity-lg, ratio lg) and the mass differences between fragments indicative of the typical cleavage reactions of chemical moieties. Both types of information were used to train DTs with or without the use of RI information.

Decision tree generation

Using the Microsoft SQL 2008 Server Analysis Services™, DTs were trained for selected single structural features. Because an SQL Server table is limited to 1,024 columns, the predictor variables had to be pre-selected. We used the Fisher ratio, F r, for ranking the variables, with

with m ₁ and m ₂ equal to the means, and v ₁ and v ₂ representing the variances of classes 1 and 2 (functional group present/absent), respectively (Duda and Hart 1973; Varmuza and Werther 1996).

In total, 138,599 Fisher scores were computed for each functional group for the evaluation of the respective discriminative power of all available mass spectral features. The 1,000 highest scoring spectral features were chosen for each prediction task. When multiple feature types were used for DT training, features of each type were selected in equal proportions.

The final training set submitted to the DT algorithm comprised 1,004 columns with 1,000 columns of the pre-selected best scoring mass spectral features. Two columns containing the optional RI-related information from the VAR5- and MDN35-RI systems were added, while one column contained the present or absent call of the structural feature under investigation. The forth column comprised the primary key reference to the respective mass spectrum entry within the GMD. Three DT training procedures were performed, generating a DT without RI information, and two DTs with RI information of either the VAR5 or the MDN35 chromatographic systems. For DTs with RI information, only those MSTs with available RI information were considered.

The DT models including selected features, transformations and other pre-processing details were saved to the server for subsequent application to user submitted MSTs of unknown structure. In the current build, the DT algorithm of the Microsoft SQL Server Analysis Services™ was parameterized according to default recommendations (cf. Table 1). The minimum node support was set to 10 spectra (tree expansion break off criterion).

Table 1 Microsoft SQL Server Analysis Services™ DT Algorithm default recommendations (cf. http://msdn.microsoft.com/en-us/library/cc645868.aspx)

Performance measures

The prediction performance was assessed by the precision ( p) and recall ( r) measures, with

and

TP, FP, TN, and FN define true positive, false positive, true negative and false negative predictions, respectively.

For a combined characterization of precision and recall, the F pr-value with

was computed as a frequently used performance measure in the field of information retrieval (van Rijsbergen 1979). F pr-values of 1 indicate optimal, while values approaching zero correspond to minimal prediction performance.

Matthews correlation coefficient (MCC) is commonly used for the assessment of binary classifications and was shown to be robust with regard to imbalanced class distributions (Matthews 1975). MCC can be computed from the contingency table according to Eq. 5:

MCC values range from −1 (perfect inverse prediction) to +1 (perfect prediction). A coefficient of 0 represents an average random prediction. The error rate obtained in cross validation (CV), Er CV, is computed as

and should approach zero with increasing DT quality.

Results and discussion

We developed DT-based substructure prediction as a potentially powerful tool box for the structural characterisation of the numerous non-identified MSTs that are encountered in routine GC-MS based metabolite profiles. To enable evidence based substructure prediction we utilized the rich resource of mass spectra and RI information of authenticated reference compounds from the GMD. Application of supervised machine learning approaches required updating of the GMD with structure information of the contained metabolites. This added information now allows binary partitioning of the known metabolites into training data that either contain or do not contain the assessed substructure. The application of supervised machine learning algorithms, such as the DT classification, now supports the in silico characterisation of yet non-identified MSTs that are frequently recognised as relevant marker molecules by non-targeted metabolite profiling. In order to define and compare the performance of the chosen DT-classification approaches for the potential users of the GMD web site and the offered web services (cf. Sect. 3.2) we report in the following the implemented CV procedures of the provided DTs, the respective feature usage and finally assess typical application cases.

Evaluation of the decision tree performance

For the characterization and comparison of DT performance, we implemented measures based on the subtotal TP, FN, FP, and TN prediction of the CV contingency table. These measures allow the assessment of alternative DTs for identical substructures, e.g. DTs with or without use of RI information. Also the quality of DTs was made comparable between different substructure predictions as two general classification errors exist, (1) deficiencies resulting from imperfect MST training data, and (2) deficiencies due to imbalanced training data. Both potential errors lead to over-fitted DTs and may compromise substructure predictions.

Cross validation of decision trees

For the reasons stated above a 50-fold CV by iterative exclusion of randomly chosen MSTs was routinely implemented. As an alternative we explored a heuristic validation process by excluding all technological replicate MSTs of single analytes (data not shown). As GMD will provide a steadily increasing number of replicate mass spectra and RIs of each analyte that is expected to improve DT-based substructure prediction we decided to use random choice of MSTs for CV. Our implemented procedure characterises DT performance and enables the calculation of overall error estimates for each substructure tree. CV results are displayed along with the respective DT information and visualization (Fig. 2). In order to demonstrate the implementation of the chosen CV process, exemplary results of a ten-fold CV are shown for the DT-classification which assesses the presence or absence of an amine moiety (Table 2).

Two synchronized web controls facilitate the access to all currently available DTs and sub-structure predictions provided by the GMD interface. The upper table describes the investigated functional group, with optional use of the RI system, and records the date of DT generation, as well as the number and proportion of MSTs linked to present calls of the substructure. DTs are characterised by a 50-fold CV error, with corresponding precision, recall, F-measure and MCC values (cf. Sect. 2.2.4). The table can be sorted by activating the selected column header. The full list of all available DTs is accessible through a paging function at the left bottom of the display. An exemplary DT trained to classify mass spectra with regard to the presence or absence of the amine substructure using the VAR5 RI information and 582 mass spectra representing metabolites with an amine moiety contrasted by 1,662 MSTs of metabolites lacking this moiety. The DT is depicted on the left hand side. Standard SQL Server Analysis Services Decision Tree properties of the activated tree node (indicated by underlay) are displayed to the right. Light (green) bars indicate the proportion of MSTs linked to a compound with amine moiety; dark (red) indicate the proportion of MSTs linked to compounds lacking this moiety. Note that we use the terms Decision Tree and Mining Model to indicate the data structures according to the SQL Server Analysis Services terminology

Table 2 Contingency table of a 10-fold CV, evaluating both performance and robustness of the DT model for the prediction of a amine sub-group generated using the February 3, 2009 version of GMD and the RI information of the VAR5 chromatographic system, in short the DT model named '2009-02-03T232942_Fg_Amine_VAR5'

Using a Precision-Recall-Plot (Fig. 3), we evaluated precision and recall in relation to an F pr-measure threshold = 0.65 using the most recent set of DTs which includes the RI information based on standardization by 9 n-alkanes (C10-C36) and a 5%-phenyl-95%-dimethylpolysiloxane capillary column, in short VAR5 (Strehmel et al. 2008).

Precision-Recall-plot for a set of DTs including the Var5 RI information of the May 2009 version of the GMD. We introduced an F pr-measure threshold of 0.65 indicated by a line as a minimal performance criterion. DTs not passing this threshold, namely the DTs for the prediction of the carboxylic acid ester, hemiacetal and heterocycle substructures were disabled for public use. The nomenclature of the chemical substructures is according to the definitions made by the checkmol program (Feldman et al. 2005)

Precision and recall measures were high for almost all tested substructures. Only the three DTs targeting the prediction of heterocycle, hemiacetal, and carboxylic acid ester substructures resulted in slightly inferior DT-classifications. These substructures are, hence, excluded from for public use and will be targeted by future efforts to enhance the DT applied algorithm and by extending the GMD compendium by suitable training data. Since the hard to classify substructures were also those with the smallest number of available training data, we expect that extended data resources within GMD will immediately result in improved performance of substructure prediction.

Analysis of MST feature usage

Table 3 summarizes the MST feature usage within the current set of DTs compiled from the May 2009 GMD version including the Var5 RI information as input variables. All DTs were generated independently, i.e. both data processing and feature pre-selection was performed separately for each substructure prior to DT training. As expected, the features incorporated in the DT vary considerably between the different predicted substructures. The usage of characteristic mass spectral fragments ( m/z) agrees with the chemical nature and hierarchy of the investigated biochemical moieties. The repeated use of characteristic mass fragments for similar substructures is apparent. For example, the fragment m/z = 99 is consistently used for all amine-like substructures, m/z = 103 in the case of alcohol-like substructures and m/z = 160 for carbonyl-like substructures. These mass fragments may be termed canonical masses which result from fragmentation reactions that are typical of compounds belonging to certain chemical classes. For example, the mass fragment m/z = 160 represents methoxyaminated aldehyde moieties, which are characteristic for reducing aldose-sugars. The fragment m/z = 103 is an abundant and typical cleavage product of trimethylsilylated primary alcohols, such as non-cyclic sugar and polyol molecules. While m/z = 103 represents the cation [.CH ₂O(TMS)⁺] and m/z = 160 the cation [C=NOCH ₃-CH ₂O(TMS)⁺], the source and usage of m/z = 99 for the prediction of amines is not yet fully understood.

Table 3 Summary of MST feature usage over a set of DTs predicting several chemically similar substructures

Considering the available choice of the numerous preselected 1,001 variables (a maximum of 1,000 spectral features plus one optional RI information), the DT classification uses only a comparatively small and specific number (<14) of selected features per DT (cf. summary row* ¹) in Table 3). This small number and the frequent choice of specific features represent an additional safe-guard against the risk of DT over-fitting. The chemical analysis and interpretation of feature usage and the analysis of the surprising absence of " ratio- lg " criteria from the current DTs is in progress but was deemed beyond the scope of this study.

Application cases

In order to characterize the potential, but also the caveats of substructure predictions using the DT algorithm provided by GMD, we performed typical application cases. New or non-identified metabolites will-in most cases-be discovered as automatically deconvoluted mass spectra from profiles of complex biological samples. Automated mass spectral deconvolution represents the typical solution to the task of analysing GC-MS profiles of highly complex biological matrices; here we use the term matrix to refer to the sum of all monitored metabolites from a biological sample. Under such conditions the available deconvolution algorithms may remove specific mass fragments either because of low compound abundance, resulting in a low signal-to-noise ratio (S/N), or due to chromatographic peak shape artefacts. Alternatively, mass fragments belonging to chromatographically co-eluting compound(s) may be incorrectly added.

Because of the ease of automated deconvolution, the experimental scientist might be tempted to base an initial structural elucidation attempt on such potentially compromised MSTs. To demonstrate the risk of such an approach, we selected four compounds derived from two complex plant matrices, namely potato tuber and rice leaf. The compounds glucose (1MeOX 5TMS), citric acid (4TMS), valine (2TMS) and putrescine (4TMS) were chosen to represent carbohydrates, organic acids, amino acids, and amines as typical metabolite classes. These metabolites contain most of the frequently occurring metabolite substructures, which have been targeted by our DT classification approach. Figure 4 shows the metabolite structures before chemical derivatization by methoxiamination (carbonyl modifying) and trimethylsilylation (substituting protons bound to heteroatoms).

Exemplary metabolites and typical substructures retrieved by the checkmol program (Feldman et al. 2005). a glucose: primary alcohol, secondary alcohol, 1,2 diol, aldehyde, hydroxyl, carbonyl, alcohol; b citric acid: carboxylic acid, hydroxyl, alcohol; c valine: alpha-amino acid, amine, carboxylic acid, hydroxyl, carbonyl, alcohol; d putrescine: primary aliphatic amine, amine

Our application cases convincingly demonstrate that many expected substructures are recognized with high reproducibility (Table 4). Nevertheless, clear differences with regard to the biological source or concerning single automated deconvolutions from identical sources exist. For example, the primary alcohol substructure is only recognized in part of the deconvolutions representing glucose and fructose, whereas the more generalized substructures containing OH-moieties are diagnosed with high repeatability. In addition, the alpha-amino acid and the more general amine-substructure were recognized with varying reproducibility, in some cases only in a small part of the automated deconvolutions.

Table 4 Reproducibility of substructure predictions using automatically deconvoluted MSTs from routine metabolite profiles of complex biological matrices (selected examples)

In conclusion, automated mass spectral deconvolutions should only be considered with care and for a preliminary substructure assessment. We recommend the use of manually curated mass spectra and a statistical evaluation of multiple high quality mass spectra best obtained from multiple biological sources or from at least two different chromatographic systems. Specialized laboratories may avoid many GC co-elution artefacts by application of a two dimensional (GCxGC)-TOF-MS system.

Availability, GMD web site and web services

All DTs developed as part of this study have been made freely available to academic users for spectra-based compound annotation at http://gmd.mpimp-golm.mpg.de/. For automated batch processing, the developed platform-independent Simple Object Access Protocol (SOAP) based web service endpoint wsPrediction provides public access to the functionality presented here.

Concluding remarks

The mass spectrum and RI compendium of the GMD has been used as a training data set for a supervised machine learning approach using a DT algorithm for the classification of MSTs and the retrieval of human-interpretable classification rules. The new GMD frontend provides a rich set of substructure classification models comprising mass spectral patterns and optional extensions including RI information, which group MSTs with common substructures. The offered DTs are provided as an extension to the conventional hit list based mass spectral matching approach and can be used to support the interpretation of MSTs from known metabolites and also facilitate the classification of those MSTs, which are not yet identified. The classification tools of the GMD frontend can be updated with the continuously growing set of GMD entries. The success of updating efforts can be assessed using DT cross validation (CV) parameters, such as precision, recall, F pr-measure, MCC, and the CV error, which have been implemented in the course of this project to compare DT performance and to reject weak prediction models. Hence, this new web interface and application may contribute to the evidence-based classification of non-identified MSTs and follows the general recommendations of the metabolomics standards initiative for reporting standards for chemical analysis (Sumner et al. 2007).

The DTs presented in this work depend on the continued curation and enhancement of the GMD content. Specifically, residual deconvolution errors will be removed, the spectral quality improved and the number of high quality replicate spectra for existing MSTs extended. Most importantly, new metabolites will be added to the GMD compendium. As a consequence, these efforts will necessitate an updating scheme for the DT substructure predictions and evaluation of DT performance will become a frequent use case.

Furthermore, the extension towards DT analyses of those substructures, which are underrepresented in the current GMD dataset, appears to be an attractive goal. Finally, given the availability of multiple DTs for the prediction of one particular substructure, the application of DT forests may be worthwhile.

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Acknowledgements

The authors acknowledge the long standing support and encouragement by Prof. L. Willmitzer, Prof. M. Stitt and Prof. R. Bock, Max Planck Institute of Molecular Plant Physiology (MPI-MP), Am Muehlenberg 1, D-14476 Potsdam-Golm, Germany. The authors thank Dr. D. Steinhauser, Dr. A. R. Fernie, A. Erban, I. Fehrle, J. Hannemann and M. Kuczmierczyk for the generation of metabolite structures and the interactive discussions during project realization.

Funding

This work was supported by the Max Planck Society, the QuantPro program of the Bundesministerium für Bildung und Forschung (BMBF), sub-project "InnOx-Innovative diagnostic tools to optimise potato breeding: Systematic analysis of cellular processes and their relation to plant internal oxygen concentrations", FKZ 0313813A, and the European META-PHOR project, FOOD-CT-2006-036220.

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This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

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1. Department Prof. L. Willmitzer, Max Planck Institute of Molecular Plant Physiology, Am Muehlenberg 1, 14476, Potsdam-Golm, Germany

Jan Hummel, Nadine Strehmel, Dirk Walther & Joachim Kopka

2. Institute for Biochemistry and Biology, University of Potsdam, Karl-Liebknecht-Strasse 24-25, Haus 20, 14476, Potsdam-Golm, Germany

Joachim Selbig

Corresponding author

Correspondence to Joachim Kopka.

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GC-MS analysis of bio-active compounds in methanolic extract of Lactuca runcinata DC

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GC-MS analysis of bio-active compounds in methanolic extract of Lactuca runcinata DC

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ORIGINAL ARTICLE

Year : 2014 | Volume : 6 | Issue : 1 | Page : 58-61

GC-MS analysis of bio-active compounds in methanolic extract of Lactuca runcinata DC

Lakshmi Kanta Kanthal ¹, ¹ Department of Pharmacology, Koringa College of Pharmacy, Tallarevu Mandal, Andhra Pradesh, India
² Department of Pharmacy, Annamalai University, Chidambaram, Tamil Nadu, India
³ Department of Pharmaceutical Chemistry, Koringa College of Pharmacy, Tallarevu Mandal, Andhra Pradesh, India
⁴ Department of Biotechnology, Koringa College of Pharmacy, Tallarevu Mandal, Andhra Pradesh, India
Akalanka Dey ², K Satyavathi ³, P Bhojaraju ⁴

Date of Submission

04-Apr-2013

Date of Acceptance

25-Apr-2013

Date of Web Publication

12-Dec-2013

Correspondence Address:
Lakshmi Kanta Kanthal
Department of Pharmacology,Koringa College of Pharmacy, Korangi 533 461,Tallarevu Mandal, Andhra Pradesh
India

Source of Support: None, Conflict of Interest: None

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DOI: 10.4103/0974-8490.122919

Background: The presence of phytochemical constitutes has been reported from species of the Compositae (Asteraceae). Hitherto no reports exist on the phytochemical components and biological activity of Lactuca runcinata DC. Objective: The present study was designed to determine the bioactive compounds in the whole plant methanol extract of Lactuca runcinata. Materials and Methods: Phytochemical screening of the entire herb of Lactuca runcinata DC revealed the presence of some bio-active components. Gas chromatography-mass spectrometry (GC-MS) analysis of the whole plant methanol extract of Lactuca runcinata was performed on a GC-MS equipment (Thermo Scientific Co.) Thermo GC-TRACE ultra ver.: 5.0, Thermo MS DSQ II. Results: The phytochemical tests showed the presence of alkaloids, cardiac glycosides, flavonoids, phenols, phlobatannin, reducing sugars, saponins, steroids, tannins, terpenoids, volatile oils, carbohydrates, and protein/amino acids in methanolic extract of L. runcinata. The GC-MS analysis has shown the presence of different phytochemical compounds in the methanolic extract of Lactuca runcinata. A total of 21 compounds were identified representing 84.49% of total methanolic extract composition. Conclusion: From the results, it is evident that Lactuca runcinata contains various phytocomponents and is recommended as a plant of phytopharmaceutical importance.

Keywords: GC-MS analysis, Lactuca runcinata, phytochemical screening, whole plant methanol extract

How to cite this article:
Kanthal LK, Dey A, Satyavathi K, Bhojaraju P. GC-MS analysis of bio-active compounds in methanolic extract of Lactuca runcinata DC. Phcog Res 2014;6:58-61

How to cite this URL:
Kanthal LK, Dey A, Satyavathi K, Bhojaraju P. GC-MS analysis of bio-active compounds in methanolic extract of Lactuca runcinata DC. Phcog Res [serial online] 2014 [cited 2020 Oct 26];6:58-61. Available from: http://www.phcogres.com/text.asp?2014/6/1/58/122919

Use of plants as a source of medicine has been inherited and is an important component of the health care system. India is the largest producer of medicinal herbs and is appropriately called the botanical garden of the world. [1] Plants used for traditional medicine contain a wide range of substances that can be used to treat chronic as well as infectious diseases. [2] Lactuca runcinata DC [L. runcinata, Synonym-Lactuca heyneana DC.] commonly known as Undirachakam [3] or Atheli is an annual erect herb belonging to the family Compositae (Asteraceae). Traditionally this plant finds its wide applicability as diuretic and in chronic obstruction of liver and bowel. [3] Lactuca runcinata DC also has been reported to be a valuable source of essential nutrients, such as carbohydrate, protein, fat etc., and micro-nutrients like calcium, iron, phosphorous etc., Chewing Lactuca runcinata with betel leaf is useful to cure the blisters of mouth and tongue.

In the last few years, gas chromatography mass spectrometry (GC-MS) has become firmly established as a key technological platform for secondary metabolite profiling in both plant and non-plant species. [4],[5],[6] A detailed literature review on the plant in investigation has shown that so far there are no published reports worldwide, related to the possible chemical components of "Lactuca runcinata". So, the present study was aimed to investigate the possible chemical components by first preparing the methanolic extract and separation and identification of the compounds by subjecting it to GC-MS analysis.

Collection of plant material

The entire herb of Lactuca runcinata DC plant (healthy and disease free plant samples) was collected from the nearby area of Thoothukudi District fields (Tamil Nadu) in December 2011, identified and authenticated by Dr. V. Chelladurai (Retired Research Officer-Botany, Central Council for Research in Ayurveda and Sidda, Govt. of India), Tiruneveli, Tamil Nadu, India. Herberium of the plant, Lactuca runcinata, was prepared and preserved in the Department of Pharmacognosy, Koringa College of Pharmacy, Korangi, East Godavari District, Andhra Pradesh, India.

Preparation of extracts

A portion of dried aerial parts (100 g) of Lactuca runcinata DC was placed in a soxhlet apparatus. Extraction was performed with 750 ml of methanol for 48 h at a temperature not exceeding the boiling point of the solvent. Extract was filtered through a 45 μm filter. [7] The resulting solution was concentrated in vacuum to dryness to give methanol extract (9 g). The extract was stored in a refrigerator at 4°C for further use.

Preliminary phytochemical screening

The methanol extract was tested for alkaloids, anthroquinones, flavonoids, phenols, steroids, tannins, terpenoids, [8] cardiac glycosides, saponins, [9] phlobatannin, [10] reducing sugars, [11] volatile oils, [12] carbohydrates and protein/amino acids. [13],[14]

GC-MS (Gas Chromatography-Mass Spectrometry) analysis

The phytochemical investigation of methanolic extract was performed on a GC-MS equipment (Thermo Scientific Co.) Thermo GC-TRACE ultra ver.: 5.0, Thermo MS DSQ II. Experimental conditions of GC-MS system were as follows: TR 5-MS capillary standard non-polar column, dimension: 30Mts, ID: 0.25 mm, Film thickness: 0.25μm. Flow rate of mobile phase (carrier gas: He) was set at 1.0 ml/min. In the gas chromatography part, temperature programme (oven temperature) was 40°C raised to 250°C at 5°C/min and injection volume was 1 μl. Samples dissolved in chloroform were run fully at a range of 50-650 m/z and the results were compared by using Wiley Spectral library search programme.

Results and Discussion

After the successful conventional hot soxhlet extraction of the whole part of the plant in investigation, the preliminary phytochemical study revealed that methanolic extract of Lactuca runcinata contains alkaloids, cardiac glycosides, flavonoids, phenols, phlobatannin, reducing sugars, saponins, steroids, tannins, terpenoids, volatile oils, carbohydrates, and protein/amino acids. Anthraquinones were absent in the L. runcinata methanolic extract, as summarized in [Table 1].

Table 1: Preliminary phytochemical evaluation of methanol extracts of Lactuca runcinata

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The results pertaining to GC-MS analysis of the methanolic extract of Lactuca runcinata DC lead to the identification of a number of compounds. These compounds were identified through mass spectrometry attached with GC. The various components present in the entire herb of Lactuca runcinata DC that were detected by the GC-MS are shown in [Table 2]. Carbetapentane, 2-Propenoic acid, 2-methylpropyl ester, Bornyl Acetate, Anti (9, 10)-tricyclo [4.2.1.1 (2, 5)] dec-3-en-9-endo-ol, Methanone (1-hydroxycyclohexyl) phenyl, 1 (3H)-Isobenzofuranone, 3-ethoxy, 4-Hydroxy-6-(4-methoxyphenyl)-2-pyranone, 1-Benzoyl-3-(4'- nitrophenyl) pyrrolo [2,1-a] phthalazine, Pyridine-3-carboxamide, 2, 4, 6-trichloro-N-(4-methoxyphenyl)- 5-nitro, Ethyl1-benzyl-4,6-dibromo-3-methyl-2-oxoindoline- 3-carboxylate, 5à-Cholestan-19-oic acid, 2à- hydroxy-, acetate, (5α) Pregnane-3,20α-diol, 14à,18à-[4-methyl-3-oxo-(1-oxa-4-azabutane-1,4-diyl)]-, diacetate, O, O-Diethyl (1 formylpentyl) selenophosphate, 3-n-Pentadecyl-2,4-dinitrophenol, 1,3-Bis (4-nitrobenzyl)-5,6 dihydrobenzo[f] quinazoline, 1,2-Benzenedicarboxylic acid, bis (2-ethylhexyl) ester, Dibenzoxazabicycloundecane, 4-[p-Bis (2-hydroxyethyl) amino] phenyl-1-bromoisoquinoline, Ergost-25-ene-3,6-dione, 5,12-dihydroxy-, (5à,12α), Lup-20 (29)-en-3-ol, acetate, (3α)-, E-Ethyl (Z)-3-(4- Acetylphenylthio) cinnamate were present in the methanolic extracts of Lactuca runcinata. The composition determined for this methanolic extract corresponds to 86.63% of the entire GC-MS chromatogram.

Table 2: Compounds identified in the methanolic extract of Lactuca runcinata In GC-MS

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The GC-MS spectrum confirmed the presence of various components with different retention times as illustrated in [Figure 1]. The mass spectrometer analyzes the compounds eluted at different times to identify the nature and structure of the compounds. The large compound fragments into small compounds giving rise to appearance of peaks at different m/z ratios. These mass spectra are fingerprint of that compound which can be identified from the data library.

The present study helps to predict the formula and structure of 21 biomolecules. Further investigation may lead to isolation of bio-active compounds and their structural elucidation and screening of pharmacological activity will be helpful for further drug development.

The presence of various bio-active compounds detected after GC-MS analysis using the methanolic extract of Lactuca runcinata DC justifies the use of whole plant for various elements by traditional practitioner.

However, isolation of individual phytochemical constituents and subjecting it to the biological activity will be definitely giving fruitful results and will open a new area of investigation of individual components and their pharmacological potency. From these results, it could be concluded that "Lactuca runcinata" contains various bio-active compounds. Evaluation of pharmacological activity is under progress. Therefore, it is recommended as a plant of phytopharmaceutical importance.

The authors are thankful to the Management of Koringa college of pharmacy, Korangi, Andhra Pradesh, India for availing all the facilities and are also thankful to Radiant Research Services Pvt. Ltd, Srinagar, Bangalore - 560 050, India for carrying out GC-MS analysis of the sample.

13.
Harborne JB. Phytochemical Methods. 2 nd ed., New York, London: Springer, Chapman and Hall; 1984.p.288.

[Figure 1]

[Table 1], [Table 2]

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GC-MS analysis of bio-active compounds in methanolic extract of Lactuca runcinata DC Kanthal LK, Dey A, Satyavathi K, Bhojaraju P - Phcog Res

# data[].attributes.attachment_link.description
ORIGINAL ARTICLE Year : 2014 | Volume : 6 | Issue : 1 | Page : 58-61 GC-MS analysis of bio-active compounds in methanolic extract of Lactuca runcinata DC Lakshmi Kanta Kanthal 1, 1 Department of Pharmacology, Koringa College of Pharmacy, Tallarevu Mandal, Andhra Pradesh, India 2 Department of Pharmacy,

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A gas chromatography-mass spectrometry based study on urine metabolomics in rats chronically poisoned with hydrogen sulfide 

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A gas chromatography-mass spectrometry based study on urine metabolomics in rats chronically poisoned with hydrogen sulfide 

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Highlights

*

We developed a serum metabonomic method to evaluate the effect of chronic poisoning by H ₂ S in rats.

*

We observed changes of lipid, energy and amino metabolism after the chronic H ₂ S poisoning.

*

GC/MS-based metabonomic methods may provide novel detection means for chronic H ₂ S poisoning.

Abstract

Hydrogen sulfide poisoning is a common occupational hazard, whose mortality and incidence rates are first and second, respectively, among occupational poisoning incidents in China. The main target organs of its toxicity are in the central nervous system and respiratory system. However, there are currently no specific direct tests that can be used to diagnose poisoned patients. In this study, we developed a serum metabonomic method using orthogonal partial least squares-discriminate analysis (OPLS-DA), based on gas chromatography-mass spectrometry (GC/MS) to evaluate the effect of chronic poisoning by hydrogen sulfide in rats. The OPLS-DA data demonstrated that the model group (n = 60) differed significantly from the control group (n = 30), suggesting that the metabolic profiles of the two groups are markedly different. Alterations in the levels of some metabolites such as citrate, galactose, lactate, mannose, inositol, urea, phosphate, alanine and valine were detected by OPLS-DA analysis. We observed changes in metabolic pathways including lipid metabolism, energy metabolism and amino metabolism in the model group. Our results indicate that GC/MS-based metabonomic methods may provide novel detection means for chronic hydrogen sulfide poisoning.

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A gas chromatography-mass spectrometry based study on serum metabolomics in rats chronically poisoned with hydrogen sulfide

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We developed a serum metabonomic method to evaluate the effect of chronic poisoning by H 2 S in rats. * We observed changes of lipid, energy and amino metabolism after the chronic H 2 S poisoning. * GC/MS-based metabonomic methods may provide novel detection means for chronic H 2 S poisoning.

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Extraction and GC/MS Analysis of the Human Blood Plasma Metabolome

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Extraction and GC/MS Analysis of the Human Blood Plasma Metabolome

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Department of Medical Biosciences, Clinical Chemistry, Umeå University, SE-90185, Umeå, Sweden, Research Group of Chemometrics, Department of Chemistry, Umeå University, SE-90187, Umeå, Sweden, Umeå Plant Science Centre, Department of Forest Genetics and Plant Physiology, Umeå University, SE-90187, Umeå, Sweden, and Department of Clinical Pharmacology, the First Affiliated Hospital of Nanjing Medical University, 210029, Nanjing, China

Cite this: Anal. Chem. 2005 , 77 , 24 , 8086-8094

Copyright © 2005 American Chemical Society

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Abstract

Analysis of the entire set of low molecular weight compounds (LMC), the metabolome, could provide deeper insights into mechanisms of disease and novel markers for diagnosis. In the investigation, we developed an extraction and derivatization protocol, using experimental design theory (design of experiment), for analyzing the human blood plasma metabolome by GC/MS. The protocol was optimized by evaluating the data for more than 500 resolved peaks using multivariate statistical tools including principal component analysis and partial least-squares projections to latent structures (PLS). The performance of five organic solvents (methanol, ethanol, acetonitrile, acetone, chloroform), singly and in combination, was investigated to optimize the LMC extraction. PLS analysis demonstrated that methanol extraction was particularly efficient and highly reproducible. The extraction and derivatization conditions were also optimized. Quantitative data for 32 endogenous compounds showed good precision and linearity. In addition, the determined amounts of eight selected compounds agreed well with analyses by independent methods in accredited laboratories, and most of the compounds could be detected at absolute levels of ∼0.1 pmol injected, corresponding to plasma concentrations between 0.1 and 1 μM. The results suggest that the method could be usefully integrated into metabolomic studies for various purposes, e.g., for identifying biological markers related to diseases.

Identified compounds in human blood plasma. This material is available free of charge via the Internet at http://pubs.acs.org.

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This article is cited by 347 publications.

1. Elin Thysell, Elin Pohjanen, Johan Lindberg, Ina Schuppe-Koistinen, Thomas Moritz, Pär Jonsson, Henrik Antti. . Omics A Journal of Integrative Biology

2. . Current literature in mass spectrometry. Journal of Mass Spectrometry

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Extraction and GC/MS Analysis of the Human Blood Plasma Metabolome

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Analysis of the entire set of low molecular weight compounds (LMC), the metabolome, could provide deeper insights into mechanisms of disease and novel markers for diagnosis.

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Derivatization of carbohydrates for GC and GC–MS analyses

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Derivatization of carbohydrates for GC and GC–MS analyses

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Abstract

GC and GC-MS are excellent techniques for the analysis of carbohydrates; nevertheless the preparation of adequate derivatives is necessary. The different functional groups that can be found and the diversity of samples require specific methods. This review aims to collect the most important methodologies currently used, either published as new procedures or as new applications, for the analysis of carbohydrates. A high diversity of compounds with diverse functionalities has been selected: neutral carbohydrates (saccharides and polyalcohols), sugar acids, amino and iminosugars, polysaccharides, glycosides, glycoconjugates, anhydrosugars, difructose anhydrides and products resulting of Maillard reaction (osuloses, Amadori compounds). Chiral analysis has also been considered, describing the use of diastereomers and derivatives to be eluted on chiral stationary phases.

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Derivatization of carbohydrates for GC and GC-MS analyses

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GC and GC-MS are excellent techniques for the analysis of carbohydrates; nevertheless the preparation of adequate derivatives is necessary. The different functional groups that can be found and the diversity of samples require specific methods. This review aims to collect the most important methodologies currently used, either published as new procedures or as new applications, for the analysis of carbohydrates.

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GC-MS method works on the principle that a mixture of substances can be separated when heated and the components can be identified after separation. It involves two techniques: separating the components of a sample using gas chromatography (GC) and identifying the components at a molecular level using mass spectrometry (MS). It begins with a gas chromatograph to vaporize the sample under a high temperature and separate its various components using a capillary column packed with a stationary (solid) phase and carried by an inert gas such as helium, argon, or nitrogen (mobile phase) across the column. As the components separate, they elute from the column at different times depending on their affinity with the stationary phase. Components with higher affinity with the stationary phase are strongly retained while those with low affinity migrate rapidly through the column. The separated components flow into a detector called mass spectrometer where they are ionized using electron or chemical ionizers. The detector generates a measurable electrical signal, referred to as ‘peak’ (mass-to-charge ratio), that is proportional to the amount of analyte. The final readout of the peak heights called the mass spectrum is then analyzed and quantified using computer libraries of mass spectra for different compounds to identify the analytes present in the sample (Thermo Fisher Scientific, n.d.).

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It isn't very good at identifying hydrocarbons that produce similar ions and it's unable to tell optical and geometrical isomers apart. The disadvantages are compensated for by combining MS with other techniques, such as gas chromatography (GC-MS).

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1. Most GC detectors are destructive, except for MS.
2. Limited to thermally stable and volatile compounds. Most analytes have a molecular weight below 500 KDa due to some volatility issues

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1. GC-MS methods are not capable of directly analyzing drugs that are nonvolatile, polar, or thermally labile. The thermal stability of the stationary phase, metabolites and their derivatives limit the metabolome coverage derived by GC-MS
2. GC-MS analysis normally requires somewhat extensive sample preparation including chemical derivatization to increase thermal stability and volatility. Compounds are required to be sufficiently volatile to allow transfer from the liquid phase to the mobile carrier gas and thus to elute from the analytical column to the detector. Several metabolites can just be analyzed by GC-MS after derivatization and this might introduce variability and produce derivatization artifacts

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1. High sensitivity and selectivity, as well as high throughput and depth of coverage (parts per million)
2. It is an excellent tool for identifying unknown components in a sample or confirming their presence

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1. Due to its high efficiency, GC allows the separation of the components of complex mixtures in a reasonable time.
2. Accurate quantitation (usually sharp reproducible peaks are obtained)
3. Mature technique with many applications notes available for users.
4. Multiple detectors with high sensitivity (ppb) are available, which can also be used in series with a mass spectrometer since MS is a non-destructive technique.

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1. High-efficiency separations have been achieved with numerous commercial capillary columns
2. This technique allows achieving high-efficiency chromatographic separation and excellent limits of quantification, and it allows use of commercial mass spectral libraries for identification of sample constituents.
3. For some of the analytes, such as organic acids, GC-MS has advantages of higher specificity compared to soft ionization techniques used in LC-MS.
4. Reliable and relatively low instrument cost and maintenance
5. GC-MS analysis generally avoids the interfering effects of ion suppression on metabolite signal intensities, which is a major challenge faced by LC-MS due the high energy nature of its ionization source (typically 70 eV)

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GC-MS is ideal for the analysis of inorganic gases and aromatic solvents, detection of impurities and allergens in cosmetics.  It is also used in the synthesis of cellulose acetate, polyethylene, polyvinyl, and synthetic fibers.

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Due to its structurally significant mass spectral peaks, extended range of analyzable low volatility samples, enhanced molecular ions, and valuable isotope ratio information, GC-MS is a powerful tool for geochemical applications. GC-MS has been used to analyze the atmosphere of Venus and has also been used by the Viking program on Mars. Additionally, a chiral GC-MS system has been used by the Rosetta mission to analyze the materials in the comet 67P/Churyumov-Gerasimenko.

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Explosive detection systems in public places use GC-MS technique for the analysis and detection of chemical warfare agents.

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GC-MS can be used for the bioanalysis of body fluids to detect narcotics, barbiturates, alcohols, and drugs such as anticonvulsants, anesthetics, antihistamines, sedative hypnotics, and anti-epileptic drugs. It is also useful in detecting pollutants and metabolites in serum and in fatty acid profiling in microbes.

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Using GC-MS, fire debris analysis can be performed as per the American Society for Testing Materials (ASTM) standards. GC-MS is widely used in forensic toxicology to identify poisons and steroids in biological specimens and in anti-doping labs to detect performance enhancing drugs such as anabolic steroids.

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In the pharmaceutical industry, GC-MS is used in research and development, production, and quality control. It is used in identification of impurities in active pharmaceutical ingredients. In medicinal chemistry, GC-MS is used in the synthesis and characterization of compounds and in pharmaceutical biotechnology.

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Aromatic compounds such as fatty acids, esters, aldehydes, alcohols, and terpenes present in food and beverages can be easily analyzed using GC-MS.  The technique can also be used to detect c spoilage or contamination of food. The analysis of a wide range of oils such as lavender oil, olive oil, spearmint oil, and essential oils, perfumes, fragrances, allergens, menthol, and syrups is also possible using GC-MS.

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Monitoring environmental pollutants is a major application of GC-MS. It is widely used in the detection of dibenzofurans, dioxins, herbicides, sulfur, pesticides, phenols, and chlorophenols in air, soil, and water.

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GC-MS is used in screening tests for the detection of several congenital metabolic diseases.  It detects trace levels of compounds present in the urine of patients with genetic metabolic disorders. It can also detect the presence of oils in ointments, creams, and lotions.