GC-MS Data Sets Research Articles

A GC-MS based metabolomics approach to determine the effect of salinity on Kimchi

GC-MS datasets coupled with multivariate statistical analysis were used to investigate metabolic changes in Kimchi during fermentation and metabolic differences in Kimchi added with various amounts (0, 1.25, 2.5, and 5%) of salts. PCA score plot obtained after 1day of fermentation were clearly distinguishable by different salinity groups, implying that early fermentation speed varied according to Kimchi salinity. PLS-DA score plot from data obtained on the 50th day of fermentation also showed a clear separation, indicating metabolites of Kimchi were different according to salinity. Concentrations of lactic acid, acetic acid, and xylitol were the highest in Kimchi with 5% salinity while concentration of fumaric acid was the highest in Kimchi with 0% salinity. Rarefaction curves showed that numbers of operational taxonomic units (OTUs) in Kimchi with 5% salinity were higher than those in Kimchi with 0% salinity, implying that Kimchi with 5% salinity had more bacterial diversities. This study highlights the applicability of GC–MS based metabolomics for evaluating fermentative characteristics of Kimchi with different salinities.

Correlation and uncorrelation between hyphenated chromatographic data with chemometric tools

Correlation studies of chromatographic data are common strategies used to relate complex mixtures of chemical components. The congruence coefficient and correlation coefficient are commonly used to indicate the similarity or correlation between different hyphenated chromatograms. However, these indices typically reduce chromatograms to a single dimension, and information in the other dimensions is not fully utilized. In this work, a new technique is developed to identify possible relationships among related high-dimensional data sets using powerful chemometric tools. First, principal component analysis is used to reduce experimental noise by reconstructing the original data sets. Then, canonical correlation analysis is utilized to obtain the canonical vectors of both data sets for comparison, which makes identification of the possible relationships between the data sets easier. An orthogonal projection operation is then applied to identify both common and different information between the matrix spaces spanned by the canonical vectors. Finally, the correlation and uncorrelation indices are defined from both the chromatographic and spectral directions on the basis of the Euclidean distance of all the elements of the final projection matrices. The new indices are more representative because they are generated via the complete employment of the entire data information that is embedded in hyphenated chromatography. In contrast to the conventional coefficients, the indices proposed in this study provide improved performance in a simulated HPLC-DAD data set and 12 real GC-MS data sets of ginseng, a widely used herbal medicine. The effects of various potential factors on the results are investigated.

Joint approximate diagonalization of eigenmatrices as a high-throughput approach for analysis of hyphenated and comprehensive two-dimensional gas chromatographic data

The objective of the present work is development of joint approximate diagonalization of eigenmatrices (JADE) as a member of independent component analysis (ICA) family, for the analysis of gas chromatography-mass spectrometry (GC–MS) and comprehensive two-dimensional gas chromatography-mass spectrometry (GC×GC–MS) data to address incomplete separation problem occurred during the analysis of complex sample matrices. In this regard, simulated GC–MS and GC×GC–MS data sets with different number of components, different degree of overlap and noise were evaluated. In the case of simultaneous analysis of multiple samples, column-wise augmentation for GC–MS and column-wise super-augmentation for GC×GC–MS was used before JADE analysis. The performance of JADE was evaluated in terms of statistical parameters of lack of fit (LOF), mutual information (MI) and Amari index as well as analytical figures of merit (AFOMs) obtained from calibration curves. In addition, the area of feasible solutions (AFSs) was calculated by two different approaches of MCR-BANDs and polygon inflation algorithm (FACPACK). Furthermore, JADE performance was compared with multivariate curve resolution-alternating least squares (MCR-ALS) and other ICA algorithms of mean-field ICA (MFICA) and mutual information least dependent component analysis (MILCA). In all cases, JADE could successfully resolve the elution and spectral profiles in GC–MS and GC×GC–MS data with acceptable statistical and calibration parameters and their solutions were in AFSs. To check the applicability of JADE in real cases, JADE was used for resolution and quantification of phenanthrene and anthracene in aromatic fraction of heavy fuel oil (HFO) analyzed by GC×GC–MS. Surprisingly, pure elution and spectral profiles of target compounds were properly resolved in the presence of baseline and interferences using JADE. Once more, the performance of JADE was compared with MCR-ALS in real case. On this matter, the mutual information (MI) values were 1.01 and 1.13 for resolved profiles by JADE and MCR-ALS, respectively. In addition, LOD values (μg/mL) were respectively 1.36 and 1.24 for phenanthrene and 1.26 and 1.09 for anthracene using MCR-ALS and JADE which showed outperformance of JADE over MCR-ALS.

Combined effects of salinity, temperature and hypoxia on Daphnia magna metabolism

Metabolic changes of Daphnia magna pools due different abiotic factors linked to global climate change (salinity, temperature and hypoxia) were investigated using untargeted GC–MS and advanced chemometric strategies using a three factors two-level full factorial experimental design (DoE). Effects of these three factors and identity of the metabolites whose concentrations changed because of them were investigated. The simultaneous analysis of GC–MS data sets using Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) allowed the resolution of the elution and mass spectra profiles of a large number of D. magna metabolites. Changes in peak areas of these metabolites were then analyzed by Principal Component Analysis (PCA), by ANOVA-Simultaneous Component Analysis (ASCA) and by Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA), and the combined effects of the three investigated stressors were assessed. Results confirmed the strong influence of increasing environmental salinity levels on the D. magna metabolome. This impact was specially highlighted by changes on the cellular content of carbohydrates, fatty acids, organic acids and amino acid molecules. In contrast, these effects were less significant for the other two factors (temperature and hypoxia) at the moderate stressing experimental conditions investigated in this work when they were not combined with salinity.

Multivariate analytical figures of merit as a metric for evaluation of quantitative measurements using comprehensive two-dimensional gas chromatography–mass spectrometry

The present contribution is devoted to develop multivariate analytical figures of merit (AFOMs) as a new metric for evaluation of quantitative measurements using comprehensive two-dimensional gas chromatography–mass spectrometry (GC×GC–MS). In this regard, new definition of sensitivity (SEN) is extended to GC×GC–MS data and then, other multivariate AFOMs including analytical SEN (γ), selectivity (SEL) and limit of detection (LOD) are calculated. Also, two frequently used second- and third-order calibration algorithms of multivariate curve resolution-alternating least squares (MCR-ALS) as representative of multi-set methods and parallel factor analysis (PARAFAC) as representative of multi-way methods are discussed to exploit pure component profiles and to calculate multivariate AFOMs. Different GC×GC–MS data sets with different number of components along with various levels of artifacts are simulated and analyzed. Noise, elution time shifts in both chromatographic dimensions, peak overlap and interferences are considered as the main artifacts in this work. Additionally, a new strategy is developed to estimate the noise level using variance-covariance matrix of residuals which is very important to calculate multivariate AFOMs. Finally, determination of polycyclic aromatic hydrocarbons (PAHs) in aromatic fraction of heavy fuel oil (HFO) analyzed by GC×GC–MS is considered as real case to confirm applicability of the proposed metric in real samples. It should be pointed out that the proposed strategy in this work can be used for other types of comprehensive two-dimensional chromatographic (CTDC) techniques like comprehensive two dimensional liquid chromatography (LC×LC).

Normal–Gamma–Bernoulli peak detection for analysis of comprehensive two-dimensional gas chromatography mass spectrometry data

Compared to other analytical platforms, comprehensive two-dimensional gas chromatography coupled with mass spectrometry (GC×GC–MS) has much increased separation power for analysis of complex samples and thus is increasingly used in metabolomics for biomarker discovery. However, accurate peak detection remains a bottleneck for wide applications of GC×GC–MS. Therefore, the normal–exponential–Bernoulli (NEB) model is generalized by gamma distribution and a new peak detection algorithm using the Normal–Gamma–Bernoulli (NGB) model is developed. Unlike the NEB model, the NGB model has no closed-form analytical solution, hampering its practical use in peak detection. To circumvent this difficulty, three numerical approaches, which are fast Fourier transform (FFT), the first-order and the second-order delta methods (D1 and D2), are introduced. The applications to simulated data and two real GC×GC–MS data sets show that the NGB-D1 method performs the best in terms of both computational expense and peak detection performance.

Metabolite profiling of Makgeolli for the understanding of yeast fermentation characteristics during fermentation and aging

Makgeolli is a traditional fermented rice wine in Korea containing complex mixture of metabolites. GC–MS datasets coupled with multivariate statistical analysis were used for the first time to investigate metabolic changes in Makgeolli during fermentation and aging, and to assess the fermentative behaviors of yeast strains. Makgeolli fermentation progressed very quickly during the early fermentation period, whereas Makgeolli metabolites barely changed during the aging period for up to 70 days. During alcohol fermentation, decreased levels of glucose and phosphoric acid, along with increased levels of the other identified metabolites were observed. The levels of tryptophan, myo-inositol, malic acid, tyrosine, ornithine, leucine, glutamic acid, glutamine, citric acid, and 2-hydroxyglutaric acid in Makgeolli with the Saccharomyces cerevisiae, as well as the levels of glycerol, lactic acid, glycine, 4-hydroxyphenylethanol, and erytritol in Makgeolli with the Saccharomyces coreanus had the highest significance. This study highlights the applicability of GC–MS based metabolite profiling for monitoring Makgeolli fermentation and evaluating the fermentative characteristics of yeast strains.

Integration of multi-omics data for prediction of phenotypic traits using random forest

BackgroundIn order to find genetic and metabolic pathways related to phenotypic traits of interest, we analyzed gene expression data, metabolite data obtained with GC-MS and LC-MS, proteomics data and a selected set of tuber quality phenotypic data from a diploid segregating mapping population of potato. In this study we present an approach to integrate these ~ omics data sets for the purpose of predicting phenotypic traits. This gives us networks of relatively small sets of interrelated ~ omics variables that can predict, with higher accuracy, a quality trait of interest.ResultsWe used Random Forest regression for integrating multiple ~ omics data for prediction of four quality traits of potato: tuber flesh colour, DSC onset, tuber shape and enzymatic discoloration. For tuber flesh colour beta-carotene hydroxylase and zeaxanthin epoxidase were ranked first and forty-fourth respectively both of which have previously been associated with flesh colour in potato tubers. Combining all the significant genes, LC-peaks, GC-peaks and proteins, the variation explained was 75 %, only slightly more than what gene expression or LC-MS data explain by themselves which indicates that there are correlations among the variables across data sets. For tuber shape regressed on the gene expression, LC-MS, GC-MS and proteomics data sets separately, only gene expression data was found to explain significant variation. For DSC onset, we found 12 significant gene expression, 5 metabolite levels (GC) and 2 proteins that are associated with the trait. Using those 19 significant variables, the variation explained was 45 %. Expression QTL (eQTL) analyses showed many associations with genomic regions in chromosome 2 with also the highest explained variation compared to other chromosomes. Transcriptomics and metabolomics analysis on enzymatic discoloration after 5 min resulted in 420 significant genes and 8 significant LC metabolites, among which two were putatively identified as caffeoylquinic acid methyl ester and tyrosine.ConclusionsIn this study, we made a strategy for selecting and integrating multiple ~ omics data using random forest method and selected representative individual peaks for networks based on eQTL, mQTL or pQTL information. Network analysis was done to interpret how a particular trait is associated with gene expression, metabolite and protein data.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1043-4) contains supplementary material, which is available to authorized users.

EIder: A compound identification tool for gas chromatography mass spectrometry data

We report software entitled EIder (EI mass spectrum identifier) that provides users with eight literature reported spectrum matching algorithms for compound identification from gas chromatography mass spectrometry (GC–MS) data. EIder calculates retention index according to experimental conditions categorized by column class, column type and data type, where 9 empirical distribution functions of the absolute retention index deviation to its mean value were constructed using the National Institute of Standards and Technology (NIST) 2011 retention index database to improve the accuracy of compound identification. EIder filters compound candidates based on elementary composition and derivatization reagent, and automatically adds the molecular information of the native compound to each derivatized compound using a manually created database. When multiple samples are analyzed together, EIder performs cross-sample alignment and provides an option of using an average mass spectrum for compound identification. Furthermore, a suite of graphical user interfaces are implemented in EIder to allow users to both manually and automatically modify the identification results using experimental information at various analysis stages. Analysis of three types of GC–MS datasets indicates that the developed EIder software can improve the accuracy of compound identification.

Improved batch correction in untargeted MS-based metabolomics.

IntroductionBatch effects in large untargeted metabolomics experiments are almost unavoidable, especially when sensitive detection techniques like mass spectrometry (MS) are employed. In order to obtain peak intensities that are comparable across all batches, corrections need to be performed. Since non-detects, i.e., signals with an intensity too low to be detected with certainty, are common in metabolomics studies, the batch correction methods need to take these into account. ObjectivesThis paper aims to compare several batch correction methods, and investigates the effect of different strategies for handling non-detects.MethodsBatch correction methods usually consist of regression models, possibly also accounting for trends within batches. To fit these models quality control samples (QCs), injected at regular intervals, can be used. Also study samples can be used, provided that the injection order is properly randomized. Normalization methods, not using information on batch labels or injection order, can correct for batch effects as well. Introducing two easy-to-use quality criteria, we assess the merits of these batch correction strategies using three large LC–MS and GC–MS data sets of samples from Arabidopsis thaliana.ResultsThe three data sets have very different characteristics, leading to clearly distinct behaviour of the batch correction strategies studied. Explicit inclusion of information on batch and injection order in general leads to very good corrections; when enough QCs are available, also general normalization approaches perform well. Several approaches are shown to be able to handle non-detects—replacing them with very small numbers such as zero seems the worst of the approaches considered.ConclusionThe use of quality control samples for batch correction leads to good results when enough QCs are available. If an experiment is properly set up, batch correction using the study samples usually leads to a similar high-quality correction, but has the advantage that more metabolites are corrected. The strategy for handling non-detects is important: choosing small values like zero can lead to suboptimal batch corrections.

Metabolic profiling of Daphnia magna exposed to environmental stressors by GC–MS and chemometric tools

Climate change is a major concern for the scientific community, demanding novel information about the effects of environmental stressors on living organisms. Metabolic profiling is required for achieving the most extensive possible range of compounds and their concentration changes on stressed conditions. Individuals of the crustacean species Daphnia magna were exposed to three different abiotic factors linked to global climate change: high salinity, high temperature levels and hypoxia. Advanced chemometric tools were used to characterize the metabolites affected by the exposure. An exploratory analysis of gas chromatography-mass spectrometry (GC–MS) data was performed to discriminate between control and exposed daphnid samples. Due to the complexity of these GC–MS data sets, a comprehensive untargeted analysis of the full scan data was performed using multivariate curve resolution-alternating least squares (MCR-ALS) method. This approach enabled to resolve most of the metabolite signals from interference peaks caused by derivatization reactions. Metabolites with significant changes in their peak areas were tentatively identified and the involved metabolic pathways explored. D. magna metabolic biomarkers are proposed for the considered physical factors. Metabolites related with energy metabolic pathways including some amino acids, carbohydrates, organic acids and nucleosides were identified as potential biomarkers of the investigated treatments. The proposed untargeted GC–MS metabolomics strategy and multivariate data analysis tools were useful to investigate D. magna metabolome under environmental stressed conditions.

Toward automated chromatographic fingerprinting: A non-alignment approach to gas chromatography mass spectrometry data

In contrast to targeted analysis of volatile compounds, non-targeted approaches take information of known and unknown compounds into account, are inherently more comprehensive and give a more holistic representation of the sample composition. Although several non-targeted approaches have been developed, there's still a demand for automated data processing tools, especially for complex multi-way data such as chromatographic data obtained from multichannel detectors. This work was therefore aimed at developing a data processing procedure for gas chromatography mass spectrometry (GC–MS) data obtained from non-targeted analysis of volatile compounds. The developed approach uses basic matrix manipulation of segmented GC–MS chromatograms and PARAFAC multi-way modelling. The approach takes retention time shifts and peak shape deformations between samples into account and can be done with the freely available N-way toolbox for MATLAB. A demonstration of the new fingerprinting approach is presented using an artificial GC–MS data set and an experimental full-scan GC–MS data set obtained for a set of experimental wines.

Multivariate curve resolution-assisted GC-MS analysis of the volatile chemical constituents in Iranian Citrus aurantium L. peel

Graphical representation of the MCR-ALS mathematical decomposition for the two-dimensional GC-MS data set of one cluster.

GC–MS based metabolomics approach of Kimchi for the understanding of Lactobacillus plantarum fermentation characteristics

Abstract GC–MS datasets, coupled with multivariate statistical methods, were used to investigate metabolic changes in Kimchi during fermentation. In order to evaluate the effect of the starter culture ( Lactobacillus plantarum ) on Kimchi fermentation, the Kimchi was sterilized in an autoclave before inoculation. Increased levels of lactic acid, glycerol, pyrotartaric acid, pentanedioic acid, 2-keto-1-gluconic acid, ribonic acid, isocitric acid, and palmitic acid were observed in Kimchi during fermentation, along with decreased levels of valine, leucine, propanoic acid, threonine, proline, glutamine, citric acid, adenine, fructose, glucose, galactose, myo-inositol, and sucrose in Kimchi were observed during fermentation. Principal component analysis (PCA) score plot also showed clear differences in metabolites among Kimchi according to the starter culture. This study highlights the applicability of GC–MS based metabolomics for monitoring Kimchi fermentation and evaluating the fermentative characteristics of starter culture.

Resolving of challenging gas chromatography–mass spectrometry peak clusters in fragrance samples using multicomponent factorization approaches based on polygon inflation algorithm

Analysis of fragrance composition is very important for both the fragrance producers and consumers. Unraveling of fragrance formulation is necessary for quality control, competitor and trace analysis. Gas chromatography–mass spectrometry (GC–MS) has been introduced as the most appropriate analytical technique for this type of analysis, which is based on Kovats index and MS database. The most straightforward method to analyze a GC–MS dataset is to integrate those peaks that can be recognized by their mass profiles. But, because of common problems of chromatographic data such as spectral background, baseline offset and specially overlapped peaks, accurate quantitative and qualitative analysis could be failed. Some chemometric modeling techniques such as bilinear multivariate curve resolution (MCR) methods have been introduced to overcome these problems and obtained well resolved chromatographic profiles. The main drawback of these methods is rotational ambiguity or nonunique solution that is represented as area of feasible solutions (AFS).Polygonal inflation algorithm (PIA) is an automatic and simple to use algorithm for numerical computation of AFS. In this study, the extent of rotational ambiguity in curve resolution methods is calculated by MCR-BAND toolbox and the PIA. The ability of the PIA in resolving GC–MS data sets is evaluated by simulated GC–MS data in comparison with other popular curve resolution methods such as multivariate curve resolution alternative least square (MCR-ALS), multivariate curve resolution objective function minimization (MCR-FMIN) by different initial estimation methods and independent component analysis (ICA). In addition, two typical challenging area of total ion chromatogram (TIC) of commercial fragrances with overlapped peaks were analyzed by the PIA to investigate the possibility of peak deconvolution analysis.

헤드스페이스 고체상미량추출(Solid-Phase Microextraction)을 이용한 시판 일본소주의 휘발성 향기성분 분석

일본 증류식 소주인 본격소주 9종을 수거하여 시료의 휘발성 향기성분을 헤드스페이스-고체상미량추출법에 의해 추출한 후, 가스크로마토그래피-질량분석기를 이용하여 분석하였다. 총 76개의 성분이 동정되었고, 화학적 특성에 따라 분류하면 에스터 48종, 알코올 13종, 기타 15종의 성분이 동정되었다. 에스터와 알코올이 주요한 성분으로 분석되었으며 아세트산아이소아밀, 폼산아이소아밀, 헥산산에틸, 옥탄산에틸, 데카노산에틸, 아이소아밀알코올, 2-페닐에탄올이 주요 성분으로 나타났다. 주성분 분석 결과 제조원료에 따라 주성분1 선상에서 대맥국 원료 본격소주의 향기패턴이 큰 차이를 보이는 것으로 나타났으며 쌀과 보리 원료에 따른 향기패턴의 차이는 크게 나타나지 않았다. 본 연구의 일본 본격소주 휘발성 향기성분 분석 결과와 국내산 증류식 소주의 주요 향기성분 분석 결과를 비교분석하면 향후 국내산 증류식 소주의 품질관리에 주요한 향기성분 파악이 가능하고 이를 통해 다양한 원재료와 제조방법에 따른 증류식 소주 제품 개발이 가능하리라 여겨진다. In this study, volatile compounds in nine commercial Japanese distilled liquors (Shochu) were isolated by headspace solid-phase microexrraction (SPME) and analyzed by gas chromatography (GC) and GC-mass spectrometry (MS). A total of 76 volatile components, including 48 esters, 13 alcohols, and 15 miscellaneous components, were identified. Esters and alcohols constituted the largest groups of quantified volatiles. Differences in volatile components among the distilled liquors and possible sample grouping were examined by applying principal component analyses to the GC-MS data sets. The first and second principal components explained 77.92% of the total variation across the samples. The samples using barley koji showed higher overall concentrations of total volatile components. Additionally, the principal component analysis did not reveal any sample grouping based on the raw material used.

A potential tool for diagnosis of male infertility: Plasma metabolomics based on GC–MS

Male infertility has become an important public health problem worldwide. Nowadays the diagnosis of male infertility frequently depends on the results of semen quality or requires more invasive surgical intervention. Therefore, it is necessary to develop a novel approach for early diagnosis of male infertility. According to the presence or absence of normal sexual function, the male infertility is classified into two phenotypes, erectile dysfunction (ED) and semen abnormalities (SA). The aim of this study was to investigate the GC–MS plasma profiles of infertile male having erectile dysfunction (ED) and having semen abnormalities (SA) and discover the potential biomarkers. The plasma samples from healthy controls (HC) (n=61) and infertility patients with ED (n=26) or with SA (n=44) were analyzed by gas chromatography–mass spectrometry (GC–MS) for discrimination and screening potential biomarkers. The partial least squares-discriminant analysis (PLS-DA) was performed on GC–MS dataset. The results showed that HC could be discriminated from infertile cases having SA (AUC=86.96%, sensitivity=78.69%, specificity=84.09%, accuracy=80.95%) and infertile cases having ED (AUC=94.33%, sensitivity=80.33%, specificity=100%, accuracy=87.36%). Some potential biomarkers were successfully discovered by two commonly used variable selection methods, variable importance on projection (VIP) and original coefficients of PLS-DA (β). 1,5-Anhydro-sorbitol and α-hydroxyisovaleric acid were identified as the potential biomarkers for distinguishing HC from the male infertility patients. Meanwhile, lactate, glutamate and cholesterol were the found to be the important variables to distinguish between patients with erectile dysfunction from those with semen abnormalities. The plasma metabolomics may be developed as a novel approach for fast, noninvasive, and acceptable diagnosis and characterization of male infertility.

A peaklet-based generic strategy for the untargeted analysis of comprehensive two-dimensional gas chromatography mass spectrometry data sets

Comprehensive two-dimensional gas chromatography mass spectrometry (GC×GC-MS) is a well-established key technology in analytical chemistry and increasingly used in the field of untargeted metabolomics. However, automated processing of large GC×GC-MS data sets is still a major bottleneck in untargeted, large-scale metabolomics. For this reason we introduce a novel peaklet-based alignment strategy. The algorithm is capable of an untargeted deterministic alignment exploiting a density based clustering procedure within a time constrained similarity matrix. Exploiting minimal 1D and 2D retention time shifts between peak modulations, the alignment is done without the need for peak merging which also eliminates the need for linear or nonlinear retention time correction procedures. The approach is validated in detail using data of urine samples from a large human metabolomics study. The data was acquired by a Shimadzu GCMS-QP2010 Ultra GC×GC-qMS system and consists of 512 runs, including 312 study samples and 178 quality control sample injections, measured within a time period of 22 days. The final result table consisted of 313 analytes, each of these being detectable in at least 75% of the study samples. In summary, we present an automated, reliable and fully transparent workflow for the analysis of large GC×GC-qMS metabolomics data sets.

Analysis of different Flos Chrysanthemum tea samples with the use of two-dimensional chromatographic fingerprints, which were interpreted by different multivariate methods

Flos Chrysanthemum teas from different origins were analyzed by combining HPLC and GC-MS datasets with the aid of chemometrics, and their quality was basically evaluated.

The influence of scaling metabolomics data on model classification accuracy

Correctly measured classification accuracy is an important aspect not only to classify pre-designated classes such as disease versus control properly, but also to ensure that the biological question can be answered competently. We recognised that there has been minimal investigation of pre-treatment methods and its influence on classification accuracy within the metabolomics literature. The standard approach to pre-treatment prior to classification modelling often incorporates the use of methods such as autoscaling, which positions all variables on a comparable scale thus allowing one to achieve separation of two or more groups (target classes). This is often undertaken without any prior investigation into the influence of the pre-treatment method on the data and supervised learning techniques employed. Whilst this is useful for deriving essential information such as predictive ability or visual interpretation in many cases, as shown in this study the standard approach is not always the most suitable option available. Here, a study has been conducted to investigate the influence of six pre-treatment methods—autoscaling, range, level, Pareto and vast scaling, as well as no scaling—on four classification models, including: principal components-discriminant function analysis (PC-DFA), support vector machines (SVM), random forests (RF) and k-nearest neighbours (kNN)—using three publically available metabolomics data sets. We have demonstrated that undertaking different pre-treatment methods can greatly affect the interpretation of the statistical modelling outputs. The results have shown that data pre-treatment is context dependent and that there was no single superior method for all the data sets used. Whilst we did find that vast scaling produced the most robust models in terms of classification rate for PC-DFA of both NMR spectroscopy data sets, in general we conclude that both vast scaling and autoscaling produced similar and superior results in comparison to the other four pre-treatment methods on both NMR and GC–MS data sets. It is therefore our recommendation that vast scaling is the primary pre-treatment method to use as this method appears to be more stable and robust across all the different classifiers that were conducted in this study.