Gas Chromatography-mass Spectrometry Data Sets Research Articles

AntDAS-GCMS: A New Comprehensive Data Analysis Platform for GC-MS-Based Untargeted Metabolomics with the Advantage of Addressing the Time Shift Problem.

Over the years, a number of state-of-the-art data analysis tools have been developed to provide a comprehensive analysis of data collected from gas chromatography-mass spectrometry (GC-MS). Unfortunately, the time shift problem remains unsolved in these tools. Here, we developed a novel comprehensive data analysis strategy for GC-MS-based untargeted metabolomics (AntDAS-GCMS) to perform total ion chromatogram peak detection, peak resolution, time shift correction, component registration, statistical analysis, and compound identification. Time shift correction was specifically optimized in this work. The information on mass spectra and elution profiles of compounds was used to search for inherent landmarks within analyzed samples to resolve the time shift problem across samples efficiently and accurately. The performance of our AntDAS-GCMS was comprehensively investigated by using four complex GC-MS data sets with various types of time shift problems. Meanwhile, AntDAS-GCMS was compared with advanced GC-MS data analysis tools and classic time shift correction methods. Results indicated that AntDAS-GCMS could achieve the best performance compared to the other methods.

GCMSFormer: A Fully Automatic Method for the Resolution of Overlapping Peaks in Gas Chromatography-Mass Spectrometry.

Gas chromatography-mass spectrometry (GC-MS) is one of the most important instruments for analyzing volatile organic compounds. However, the complexity of real samples and the limitations of chromatographic separation capabilities lead to coeluting compounds without ideal separation. In this study, a Transformer-based automatic resolution method (GCMSFormer) is proposed to resolve mass spectra from GC-MS peaks in an end-to-end manner, predicting the mass spectra of components directly from the raw overlapping peaks data. Furthermore, orthogonal projection resolution (OPR) was integrated into GCMSFormer to resolve minor components. The GCMSFormer model was trained, validated, and tested using 100,000 augmented data. It achieves 99.88% of the bilingual evaluation understudy (BLEU) value on the test set, significantly higher than the 97.68% BLEU value of the baseline sequence-to-sequence model long short-term memory (LSTM). GCMSFormer was also compared with two nondeep learning resolution tools (MZmine and AMDIS) and two deep learning resolution tools (PARAFAC2 with DL and MSHub/GNPS) on a real plant essential oil GC-MS data set. Their resolution results were compared on evaluation metrics, including the number of compounds resolved, mass spectral match score, correlation coefficient, explained variance, and resolution speed. The results demonstrate that GCMSFormer has better resolution performance, higher automation, and faster resolution speed. In summary, GCMSFormer is an end-to-end, fast, fully automatic, and accurate method for analyzing GC-MS data of complex samples.

Comparison of chemometric assisted targeted and untargeted approaches for the prediction of radical scavenging activity of ylang-ylang essential oils

Essential oils have been used for centuries for their preservative properties. An example is ylang-ylang Cananga odorata [Lam.] Hook. f. & Thomson essential oil, which exists in four different distillation grades, where the fraction with the longest distillation time has the highest radical scavenging activity (RSA). Gas chromatography mass spectrometry (GC-MS) followed by multivariate statistical analysis is a powerful approach for determination of RSA. Herein the performance of such multivariate statistical analysis using three data sets derived from gas chromatography mass spectrometry (GC-MS) analysis, is compared to that achieved using two direct and fast spectroscopic techniques, for the prediction of RSA using partial least squares (PLS) regression analysis. The three GC-MS data sets were, ‘full chemical composition’, ‘total chromatogram average mass spectra (TCAMS)’ and ‘segment average mass spectra (SAMS)’, whilst two spectroscopic techniques, namely attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy and Raman spectroscopy, provided the spectroscopic data sets for comparison. PLS models created using ATR-FTIR and ‘full chemical composition’ data sets provided the lowest relative error of prediction (REP) and mean error of prediction (MEP) in validation, whilst in independent test sets, the PLS models created using ATR-FTIR and SAMS data sets delivered the lowest REP and MEP. The three GC-MS derived data sets were further compared for value in determination of compounds contributing to the RSA. PLS regression analysis of the full chemical composition data set revealed that germacrene D and (E,E)-α-farnesene were the major contributors to the RSA, whilst average mass spectrum based data sets, TCAMS and SAMS, also highlighted eugenol as another contributor to the RSA.

Development of variance rank initiated-unsupervised sample indexing for gas chromatography-mass spectrometry analysis

Traditional non-targeted chemometric workflows for gas chromatography-mass spectrometry (GC-MS) data rely on using supervised methods, which requires a priori knowledge of sample class membership. Herein, we propose a simple, unsupervised chemometric workflow known as variance rank initiated-unsupervised sample indexing (VRI-USI). VRI-USI discovers analyte peaks exhibiting high relative variance across all samples, followed by k-means clustering on the individual peaks. Based upon how the samples cluster for a given peak, a sample index assignment is provided. Using a probabilistic argument, if the same sample index assignment appears for several discovered peaks, then this outcome strongly suggests that the samples are properly classified by that particular sample index assignment. Thus, relevant chemical differences between the samples have been discovered in an unsupervised fashion. The VRI-USI workflow is demonstrated on three, increasingly difficult datasets: simulations, yeast metabolomics, and human cancer metabolomics. For simulated GC-MS datasets, VRI-USI discovered 85–90% of analytes modeled to vary between sample classes. Nineteen out of 53 peaks in the peak table developed for the yeast metabolome dataset had the same sample index assignments, indicating that those indices are most likely due to class-distinguishing chemical differences. A t-test revealed that 22 out of 53 peaks were statistically significant (p < 0.05) when using those sample index assignments. Likewise, for the human cancer metabolomics study, VRI-USI discovered 25 analytes that were statistically different (p < 0.05) using the sample index assignments determined to highlight meaningful sample-based differences. For all datasets, the sample index assignments that were deduced from VRI-USI were the correct class-based difference when using prior knowledge. VRI-USI holds promise as an exploratory data analysis workflow for studies in which analysts do not readily have a priori class information or want to uncover the underlying nature of their dataset.

QPMASS: A parallel peak alignment and quantification software for the analysis of large-scale gas chromatography-mass spectrometry (GC-MS)-based metabolomics datasets

Gas chromatography-mass spectrometry (GC-MS) is a robust analytical platform for analysis of small molecules. Recently, it is widely used for large-scale metabolomics studies, in which hundreds or even thousands of samples are analyzed simultaneously, producing a very large and complex GC-MS datasets. A number of software are currently available for processing GC-MS data, but it is too compute-intensive for them to efficiently and accurately align chromatographic peaks from thousands of samples. Here, we report a newly developed software, QPMASS, for analysis of large-scale GC-MS data. The parallel computing with an advanced dynamic programming approach is implemented in QPMASS to align peaks from multiple samples based on retention time and mass spectra, enabling fast processing large-scale datasets. Furthermore, the missing value filtering and backfilling are introduced into the program, greatly reducing false positive and false negative errors to be less than 5%. We demonstrated that it took only 8 h to align and quantify a GC-TOF-MS dataset from 300 rice leaves samples, and 17 h to process a GC-qMS dataset from 1000 rice seed samples by using a personal computer (3.70 GHz CPU, 16 GB of memory and > 100 GB hard disk). QPMASS is written in C++ programming language, and is able to run under Windows operation system with a user-friendly interface.

Discrimination of Cultivated Regions of Soybeans (Glycine max) Based on Multivariate Data Analysis of Volatile Metabolite Profiles.

Soybean (Glycine max) is a major crop cultivated in various regions and consumed globally. The formation of volatile compounds in soybeans is influenced by the cultivar as well as environmental factors, such as the climate and soil in the cultivation areas. This study used gas chromatography-mass spectrometry (GC-MS) combined by headspace solid-phase microextraction (HS-SPME) to analyze the volatile compounds of soybeans cultivated in Korea, China, and North America. The multivariate data analysis of partial least square-discriminant analysis (PLS-DA), and hierarchical clustering analysis (HCA) were then applied to GC-MS data sets. The soybeans could be clearly discriminated according to their geographical origins on the PLS-DA score plot. In particular, 25 volatile compounds, including terpenes (limonene, myrcene), esters (ethyl hexanoate, butyl butanoate, butyl prop-2-enoate, butyl acetate, butyl propanoate), aldehydes (nonanal, heptanal, (E)-hex-2-enal, (E)-hept-2-enal, acetaldehyde) were main contributors to the discrimination of soybeans cultivated in China from those cultivated in other regions in the PLS-DA score plot. On the other hand, 15 volatile compounds, such as 2-ethylhexan-1-ol, 2,5-dimethylhexan-2-ol, octanal, and heptanal, were related to Korean soybeans located on the negative PLS 2 axis, whereas 12 volatile compounds, such as oct-1-en-3-ol, heptan-4-ol, butyl butanoate, and butyl acetate, were responsible for North American soybeans. However, the multivariate statistical analysis (PLS-DA) was not able to clearly distinguish soybeans cultivated in Korea, except for those from the Gyeonggi and Kyeongsangbuk provinces.

RMet: An automated R based software for analyzing GC-MS and GC×GC-MS untargeted metabolomic data

Gas chromatography-mass spectrometry (GC-MS) and comprehensive two-dimensional gas chromatography-mass spectrometry (GC×GC-MS) are powerful techniques for measurement of all metabolites in complex metabolic samples. However, analyzing GC-MS and especially GC×GC-MS metabolomic data is a major challenge to the researchers in the field of metabolomics mainly due to the complexity and large data size. In this regard, an automated R based software entitled RMet has been developed to overcome the challenges in the metabolomic analysis workflow of GC-MS and GC×GC-MS data sets. Additionally, it is able to facilitate the complex process of extracting reliable and useful biological information from these data sets. Moreover, RMet can greatly accelerate the time-consuming data analysis process of large GC-MS and GC×GC-MS datasets by the means of modern chemometric methods. In fact, RMet transforms raw GC-MS and GC×GC-MS data files into the elution profiles and mass spectra of important (significantly affected metabolites) which can be imported into NIST MS search software for the final identification of these metabolites. To show the performance of the developed software, large GC×GC-MS data sets of a previously reported environmental metabolomics study on lettuce samples exposed to contaminants of emerging concerns (CECs) were analyzed by RMet. The procedure for analyzing GC-MS metabolic data with RMet is as same as GC×GC-MS data sets but some steps can be skipped due to the lower size of GC-MS data sets. The software, its manual, sample data sets and source code are freely available on https://github.com/SUTChemometricsGroup/RMet.

Study on volatile markers of pasta quality using GC-MS and a peptide based gas sensor array

Headspace GC-MS (gas chromatography–mass spectrometry) and peptide based gas sensor array (E-nose) analysis were performed to evaluate the volatile fraction of durum wheat pasta samples of different commercial value and furosine content. A clear discrimination among high-price and low price samples of pasta was achieved using a simple PCA (Principal Component Analysis) of GC-MS dataset. This was attributed to the quality of semolina used in the process. Linear discriminant analysis (LDA) allowed discrimination among low or high furosine content in the samples. The peptide based e-nose was tested initially on a set of pure key volatiles found in GC-MS analysis and then on pasta samples. Discrimination of pasta samples having different commercial value was achieved. This work demonstrates that volatiles can be used to determine the quality of pasta with respect to semolina and thermal treatment. Moreover, the peptide-based e-nose represents a useful tool alternative to GC-MS to detect off-flavours and quality of the semolina in pasta.

Quality attributes and chemical composition of commercial cinnamon oils

Cinnamon oil is commercially available and has been used by the food, fragrance and pharmaceutical industries. In present work, 22 batches of commercial cinnamon oils from different brands were investigated. There are no significant differences in the appearance profiles and physicochemical properties between the samples, but great variation in the content of cinnamaldehyde was found by gas chromatography quantitative analysis. They were further chemically characterised by gas chromatography-mass spectrometry (GC-MS), and 54 volatile compounds were identified. According to principal component analysis of the GC-MS data set, the samples were clustered into three distinct groups with higher concentrations of cinnamaldehyde, diethyl phthalate, and eugenol as their markers, respectively. Moreover, coumarin, a hepatotoxic natural compound, was detected in some samples, which indicated that the samples with higher level of cinnamaldehyde and limited amount of coumarin would be better choice for efficacious and safe use.

Varying Inocula Permutations (Aspergillus oryzae and Bacillus amyloliquefaciens) affect Enzyme Activities and Metabolite Levels in Koji.

In this study, we investigated the altered enzymatic activities and metabolite profiles of koji fermented using varying permutations of Aspergillus oryzae and/or Bacillus amyloliquefaciens. Notably, the protease and β-glucosidase activities were manifold increased in co-inoculated (CO) koji samples (co-inoculation of A. oryzae and B. amyloliquefaciens). Furthermore, gas chromatography-mass spectrometry (GC-MS)-based metabolite profiling indicates that levels of amino acids, organic acids, sugars, sugar alcohols, fatty acids, nucleosides, and vitamins were distinctly higher in CO, SA (sequential inoculation of A. oryzae, followed by B. amyloliquefaciens), and SB (sequential inoculation of B. amyloliquefaciens, followed by A. oryzae). The multivariate principal component analysis (PCA) plot based on GC-MS datasets indicated a clustered pattern for MA and MB (koji samples inoculated either with A. oryzae or B. amyloliquefaciens) across PC2 (20.0%). In contrast, the CO, SA, and SB metabolite profiles displayed segregated patterns across PLS1 (22.2%) and PLS2 (21.1%) in the partial least square discriminant analysis (PLS-DA) model. Intriguingly, the observed disparity in the levels of primary metabolites was engendered largely by higher relative levels of sugars and sugar alcohols in MA, SA, and CO koji samples, which was commensurate with the relative amylase activities in respective samples. Collectively, the present study emphasizes the utility of integrated biochemical and metabolomic approaches for achieving the optimal permutation of fermentative inocula for industrial koji preparation.

Geometric search: A new approach for fitting PARAFAC2 models on GC-MS data

PARAFAC2 is a powerful decomposition method which is ideally suited for modeling gas chromatography-mass spectrometry (GC-MS) data. However, the most widely used fitting algorithms (alternating least squares, ALS) are very slow which hinders use of the model. In this paper, an iterative method called geometric search is proposed to fit the PARAFAC2 model. This method models the PARAFAC2 loading parameters as geometric sequences with offsets during the ALS iterations. It extrapolates the optimal parameters from prior iterations to accelerate ALS convergence process. The performance of this method was evaluated by simulated datasets and two GC-MS datasets of wine and tobacco samples. This geometric search method proved an efficient way to fit PARAFAC2 models, compared with a standard ALS algorithm and two widely used line search algorithms in terms of convergence speed and fitting quality.

Feature extraction from resolution perspective for gas chromatography-mass spectrometry datasets

Automatic feature extraction from large-scale datasets is one of the major challenges when analyzing complex samples with gas chromatography-mass spectrometry (GC-MS).

Robust alignment of chromatograms by statistically analyzing the shifts matrix generated by moving window fast Fourier transform cross-correlation.

Retention time shift is one of the most challenging problems during the preprocessing of massive chromatographic datasets. Here, an improved version of the moving window fast Fourier transform cross-correlation algorithm is presented to perform nonlinear and robust alignment of chromatograms by analyzing the shifts matrix generated by moving window procedure. The shifts matrix in retention time can be estimated by fast Fourier transform cross-correlation with a moving window procedure. The refined shift of each scan point can be obtained by calculating the mode of corresponding column of the shifts matrix. This version is simple, but more effective and robust than the previously published moving window fast Fourier transform cross-correlation method. It can handle nonlinear retention time shift robustly if proper window size has been selected. The window size is the only one parameter needed to adjust and optimize. The properties of the proposed method are investigated by comparison with the previous moving window fast Fourier transform cross-correlation and recursive alignment by fast Fourier transform using chromatographic datasets. The pattern recognition results of a gas chromatography mass spectrometry dataset of metabolic syndrome can be improved significantly after preprocessing by this method. Furthermore, the proposed method is available as an open source package at https://github.com/zmzhang/MWFFT2.

Chapter Sixteen - Analysis of Metabolomic Profiling Data Acquired on GC–MS

Gas chromatography-mass spectrometry (GC-MS) is one of the three most popular analytical platforms for metabolomics and is largely employed for the study of oncometabolism. Large volumes of data are usually generated in a GC-MS experiment, and many analytical steps are required to extract biologically relevant information from GC-MS data. These steps include (1) spectrum deconvolution, to convert raw data into a peak list; (2) metabolite identification, to recognize metabolites associated to chromatographic peaks; (3) quantification, to compare the abundance of a specific metabolite in different samples; (4) association network analysis, to reveal correlations among the changes in the abundance of multiple metabolites; and (5) pathway analysis, to understand the biochemical interrelationship between several metabolites that vary in a coordinated or differential manner. Here, we describe in detail the analytical steps that are necessary to interpret a GC-MS dataset.

Combining peak- and chromatogram-based retention time alignment algorithms for multiple chromatography-mass spectrometry datasets

BackgroundModern analytical methods in biology and chemistry use separation techniques coupled to sensitive detectors, such as gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS). These hyphenated methods provide high-dimensional data. Comparing such data manually to find corresponding signals is a laborious task, as each experiment usually consists of thousands of individual scans, each containing hundreds or even thousands of distinct signals. In order to allow for successful identification of metabolites or proteins within such data, especially in the context of metabolomics and proteomics, an accurate alignment and matching of corresponding features between two or more experiments is required. Such a matching algorithm should capture fluctuations in the chromatographic system which lead to non-linear distortions on the time axis, as well as systematic changes in recorded intensities. Many different algorithms for the retention time alignment of GC-MS and LC-MS data have been proposed and published, but all of them focus either on aligning previously extracted peak features or on aligning and comparing the complete raw data containing all available features.ResultsIn this paper we introduce two algorithms for retention time alignment of multiple GC-MS datasets: multiple alignment by bidirectional best hits peak assignment and cluster extension (BIPACE) and center-star multiple alignment by pairwise partitioned dynamic time warping (CeMAPP-DTW). We show how the similarity-based peak group matching method BIPACE may be used for multiple alignment calculation individually and how it can be used as a preprocessing step for the pairwise alignments performed by CeMAPP-DTW. We evaluate the algorithms individually and in combination on a previously published small GC-MS dataset studying the Leishmania parasite and on a larger GC-MS dataset studying grains of wheat (Triticum aestivum).ConclusionsWe have shown that BIPACE achieves very high precision and recall and a very low number of false positive peak assignments on both evaluation datasets. CeMAPP-DTW finds a high number of true positives when executed on its own, but achieves even better results when BIPACE is used to constrain its search space. The source code of both algorithms is included in the OpenSource software framework Maltcms, which is available from http://maltcms.sf.net. The evaluation scripts of the present study are available from the same source.

Differences in the Volatile Compositions of Ginseng Species (Panax sp.)

The volatile compositions in dried white ginseng according to species (Panax ginseng, Panax notoginseng, and Panax quinquefolius) were analyzed and compared by applying multivariate statistical techniques to gas chromatography-mass spectrometry data sets. Main volatile compounds of ginseng species in the present study were sesquiterpenes, such as bicyclogermacrene, (E)-β-farnesene, β-panasinsene, calarene, α-humulene, β-elemene, etc. In particular, α-selinene, α-terpinolene, β-bisabolene, β-phellandrene, β-sesquiphellandrene, zingiberene, germacrene D, limonene, α-gurjunene, (E)-caryophyllene, δ-cadinene, (E)-β-farnesene, α-humulene, bicyclogermacrene, longiborn-8-ene, β-neoclovene, and (+)-spathulenol were mainly associated with the difference between P. ginseng and P. notoginseng versus P. quinquefolius species. On the other hand, the discrimination between P. ginseng and P. notoginseng could be constructed by hexanal, 2-pyrrolidinone, (E)-2-heptenal, (E)-2-octenal, heptanal, isospathulenol, (E,E)-2,4-decadienal, 3-octen-2-one, benzaldehyde, 2-pentylfuran, and (E)-2-nonenal.

Metabolite Profiling and Bioactivity of Rice Koji Fermented by Aspergillus Strains

In this study, the metabolite profiles of three Aspergillus strains during rice koji fermentation were compared. In the partial least squares discriminant analysis-based gas chromatography-mass spectrometry data sets, the metabolite patterns of A. oryzae (KCCM 60345) were clearly distinguished from A. kawachii (KCCM 60552) and only marginal differences were observed for A. oryzae (KCCM 60551) fermentation. In the 2 days fermentation samples, the overall metabolite levels of A. oryzae (KCCM 60345) were similar to the A. oryzae (KCCM 60551) levels and lower than the A. kawachii (KCCM 60552) levels. In addition, we identified discriminators that were mainly contributing tyrosinase inhibition (kojic acid) and antioxidant activities (pyranonigrin A) in A. oryzae (KCCM 60345) and A. kawachii (KCCM 60552) inoculated rice koji, respectively. In this study, we demonstrated that the optimal inoculant Aspergillus strains and fermentation time for functional rice koji could be determined through a metabolomics approach with bioactivity correlations.

The Compositions of Volatiles and Aroma-Active Compounds in Dried Omija Fruits (Schisandra chinensis Baillon) According to the Cultivation Areas

Differences in the compositions of volatiles from dried omija fruits (Schisandra chinensis Baillon) cultivated in different areas (Mungyeong, Jangsu, Jechon, and Hoengseong) in South Korea were determined by applying principal component analysis to gas chromatography-mass spectrometry data sets. Quantitative assessments revealed that terpene hydrocarbons, such as germacrene D, β-selinene, α-ylangene, β-elemene, α-selinene, and (E)-β-farnesene, were the main volatiles in all omija fruit samples. On the other hand, (E)-β-ocimene, calarene, (E)-β-farnesene, β-selinene, nonanal, 2-methylbutanoic acid, benzoic acid, 2,3-butanediol, and phenethyl alcohol were the major volatile components that contributed to the discrimination between omija fruit samples from the four cultivation areas. In addition, aroma-active compounds in four dried omija fruits were investigated and compared by gas chromatography-olfactometry using aroma extract dilution analysis. (E)-β-Ocimene (floral and herbaceous), α-pinene (pine-like and woody), hexanal (cut grass-like), 5-methylfurfural (burnt sugar-like and sweet), and α-terpinene (minty, green, and fresh) were important aroma-active compounds in all omija samples. Interestingly, the flavor dilution factors of most aroma-active compounds were lower for omija sample cultivated in Hoengseong than for those cultivated in Mungyeong, Jangsu, and Jechon.

Comparison of Volatile Compounds in Pepper (&lt;i&gt;Piper nigrum&lt;/i&gt; L.) by Simultaneous Distillation Extraction (SDE) and GC-MS

In this study, the volatile compounds of pepper (Piper Nigrum L.) were extracted and analyzed by simultaneous distillation-extraction (SDE) and gas chromatography-mass spectrometry (GC-MS). The differences in volatile compounds were observed by GC-MS data sets. Results showed vinyl compounds were the most dominant volatiles in pepper by simultaneous distillation-extraction (SDE) and GC-MS, and 22 volatile compounds in the white pepper and black pepper were the same.

Comparison of Fermented Soybean Paste (Doenjang) Prepared by Different Methods Based on Profiling of Volatile Compounds

In this study, 2 different extraction methods, namely solvent-assisted flavor evaporation (SAFE) and solid-phase microextraction (SPME), were employed to investigate the comprehensive volatile profile of Doenjang (one of Korean fermented soybean pastes) efficiently. Quantitatively, major volatiles of Doenjang isolated by SAFE were 3-methylbutanoic acid, butanoic acid, 3-hydroxy-2-methyl-4H-pyran-4-one (maltol), ethyl 2-methylbutanoate, 2-methylpropanoic acid, tetramethylpyrazine, and 4-ethyl-2-methoxyphenol, while ethanol, ethenylbenzene, ethyl benzoate, ethyl linoleate, ethyl acetate, ethyl butanoate, tetramethylpyrazine, and ethyl 2-methylpropanoate extracted by SPME. In addition, volatile profiling that applied principal component analysis to gas chromatography-mass spectrometry datasets allowed Doenjang samples that had been prepared using different traditional and commercial methods to be discriminated, and the volatile compounds that contributed to their discrimination were assigned. The major volatiles that were related to differentiation of traditional and commercial Doenjang samples were 2-pentylfuran, 4-ethylphenol, dihydro-5-methyl-2(3H)-furanone, butanoic acid, pyrazines (for example, 2-ethyl-5-methylpyrazine and 2,3-dimethylpyrazine), esters (for example, ethyl 4-methylpentanoate and diethyl succinate), maltol, dimethyl disulfide, 2- and 3-methylbutanal, hexanal, 4-vinylphenol, and ethanol.