Multivariate curve resolution combined with gas chromatography to enhance analytical separation in complex samples: A review

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.

Deep learning-based method for automatic resolution of gas chromatography-mass spectrometry data from complex samples

Modern gas chromatography-mass spectrometry (GC-MS) is the workhorse for the high-throughput profiling of volatile compounds in complex samples. It can produce a considerable amount of two-dimensional data, and automatic methods are required to distill chemical information from raw GC-MS data efficiently. In this study, we proposed an Automatic Resolution method (AutoRes) based on pseudo-Siamese convolutional neural networks (pSCNN) to extract the meaningful features swamped by the noises, baseline drifts, retention time shifts, and overlapped peaks. Two pSCNN models were trained with 400,000 augmented spectral pairs, respectively. They can predict the selective region (pSCNN1) and elution region (pSCNN2) of compounds in an untargeted manner. The accuracies of the pSCNN1 model and the pSCNN2 model on their test sets are 99.9% and 92.6%, respectively. Then, the chromatographic profile of each component was automatically resolved by full rank resolution (FRR) based on the predicted regions by these models. The performance of AutoRes was evaluated on the simulated and plant essential oil datasets. Compared to AMDIS and MZmine, AutoRes resolves more reasonable mass spectra, chromatograms, and peak areas to identify and quantify compounds. The average match scores of AutoRes (925 and 936) outperformed AMDIS (909 and 925) and MZmine (888 and 916) when resolving mass spectra from overlapped peaks on the Set Ⅰ and Set Ⅱ of plant essential oil dataset and matching them against the NIST17 library. It extracted peak areas and mass spectra automatically from 10 GC-MS files of plant essential oils, and the entire process was completed in 8 min without any prior information or manual intervention. It is implemented in Python and is available as an open-source package at https://github.com/dyjfan/AutoRes.

A Comparative Analysis of Analytical Techniques for Rapid Oil Spill Identification

The complex chemical composition of crude oils presents many challenges for rapid chemical characterization in the case of a spill. A number of approaches are currently used to "fingerprint" petroleum-derived samples. Gas chromatography coupled with mass spectrometry (GC-MS) is the most common, albeit not very rapid, technique; however, with GC-MS alone, it is difficult to resolve the complex substances in crude oils. The present study examined the potential application of ion mobility spectrometry-mass spectrometry (IMS-MS) coupled with chem-informatic analyses as an alternative high-throughput method for the chemical characterization of crude oils. We analyzed 19 crude oil samples from on- and offshore locations in the Gulf of Mexico region in the United States using both GC-MS (biomarkers, gasoline range hydrocarbons, and n-alkanes) and IMS-MS (untargeted analysis). Hierarchical clustering, principal component analysis, and nearest neighbor-based classification were used to examine sample similarity and geographical groupings. We found that direct-injection IMS-MS performed either equally or better than GC-MS in the classification of the origins of crude oils. In addition, IMS-MS greatly increased the sample analysis throughput (minutes vs hours per sample). Finally, a tabletop science-to-practice exercise, utilizing both the GC-MS and IMS-MS data, was conducted with emergency response experts from regulatory agencies and the oil industry. This activity showed that the stakeholders found the IMS-MS data to be highly informative for rapid chemical fingerprinting of complex substances in general and specifically advantageous for accurate and confident source-grouping of crude oils. Collectively, the present study shows the utility of IMS-MS as a technique for rapid fingerprinting of complex samples and demonstrates its advantages over traditional GC-MS-based analyses when used for decision-making in emergency situations. Environ Toxicol Chem 2021;40:1034-1049. © 2020 SETAC.

Application of MCR-ALS with EFA on FT-IR spectra of lipid bilayers in the assessment of phase transition temperatures: Potential for discernment of coupled events

Temperature-dependent transmission FT-IR spectroscopy and DSC measurements were conducted on lipid multibilayers constituted from 1,2-dipalmitoyl-sn-glycero-3-phosphocholine. Lipid multibilayers made from 1,2-dipalmitoyl-sn-glycero-3-phosphoethanolamine, which do not form a ripple phase, were examined as a reference. Spectra were analyzed using multivariate curve resolution technique with alternating least squares and evolving factor analysis (MCR-ALS with EFA) and lipid phase transition temperatures were determined. Polar parts of lipid molecules exert greater response on a ripple phase formation than non-polar ones. However, vibrational signatures of hydrocarbon chains with intramolecular origins display certain qualitative differences that pave the way for future work oriented on uncoupling the events that drive ripple phase formation.

Data handling and data analysis in metabolomic studies of essential oils using GC-MS

Gas chromatography electron impact ionization mass spectrometry (GC-EI-MS) has been, and remains, the most widely applied analytical technique for metabolomic studies of essential oils. GC-EI-MS analysis of complex samples, such as essential oils, creates a large volume of data. Creating predictive models for such samples and observing patterns within complex data sets presents a significant challenge and requires application of robust data handling and data analysis methods. Accordingly, a wide variety of software and algorithms has been investigated and developed for this purpose over the years. This review provides an overview and summary of that research effort, and attempts to classify and compare different data handling and data analysis procedures that have been reported to-date in the metabolomic study of essential oils using GC-EI-MS.

Non-target analysis of vapor mixtures using silicon nanowire array sampling and thermal desorption

This work presents and evaluates an algorithmic approach to deconvolving the elution profiles of chemical components of vapor mixtures that have been sampled and desorbed from a novel preconcentrator based on highly ordered silicon nanowire arrays. The arrays provide a medium for both preconcentration and partial chromatographic resolution, which is then further leveraged with multichannel detection. Here, mixtures of nitro aromatic vapors are sampled and then thermally desorbed from the device, at which point they are detected by a conventional mass selective detector. The overlapping elution profiles observed from the array are sequentially extracted using a chemometric analysis approach based on evolving factor analysis and multivariate curve resolution by alternating least squares, enabling qualitative and quantitative analysis of individual components without target analyte libraries or complete chromatographic separation. This work examines the analytical capabilities conferred to multichannel detection by silicon nanowire array pre-concentration and partial separation and discusses the technique's limitations, illustrated by both experimental and simulated data.

A Combined Study of Headspace Volatiles using Human Sensory, Mass Spectrometry and Chemometrics

Smokeless tobacco products (STPs) are widely used in certain parts of the world, yet there is limited understanding of how they are consumed, particularly the impact of chemosensory characteristics on their use. In order to develop an understanding of the drivers of STP use and product acceptability we conducted both human sensory panel testing and chemical analyses on a range of STPs. Free-sorting paired odour testing using sensory panellists identified similarities and clear differences between eleven different STPs. Headspace volatiles, analysed by headspace solid-phase microextraction gas chromatography mass spectrometry (HS-SPME-GC-MS), identified 20 to 70 components depending upon the STP. Key differences in headspace volatiles were found between STPs. For example, the headspace of Skoal Bandits Wintergreen was dominated by methyl salicylate, while Marlboro Spice consists of a more complex profile including pinene, nicotine, eugenol and cymene. Chemometric Target Factor Analysis (TFA) and Hierarchical Cluster Analysis (HCA) of chemistry and sensory data was used to deduce chemical drivers of sensory perceptions. The chemometric strategy used showed that headspace analysis is a complementary screening tool to sensory analysis in classification studies. This study is generic with applications across various product sectors that require routine human sensory panel evaluation.

Gas chromatography-Fourier transform infrared spectroscopy reveals dynamic molecular interconversion of oximes

This study reports gas chromatography (GC) combined with Fourier transform infrared (FTIR) spectroscopy to investigate the elution profiles of individual oxime isomers undergoing characteristic interconversion (dynamic chromatography) in GC. The use of a light-pipe FTIR interface enables on-line acquisition of FTIR spectra, which in turn render unambiguous identification of the individual molecules. Here, acetaldehyde oxime and propionaldehyde oxime were chosen for comparison of elution behaviour under varying temperature and carrier flow velocities. The choice of selective responses (wavenumber selectivity), which were relatively stronger for each isomer, enabled display and retracing of the individual isomer over the chromatographic time scale and thus provided characteristic single isomer profiles. Chemometric data analysis using the multivariate curve resolution technique further confirmed this isomer elution profile. Simulation of the spectrum for each isomer allowed comparison with instrument-generated FTIR spectra to confirm the elution order of E and Z isomers. The effect of changing chromatographic parameters (temperature, flow) on interconversion rates and/or extents were studied and the corresponding change in FTIR spectrum intensity was noted. The GC-FID data acquired concurrently with GC-FTIR analyses ratified isomerisation chromatographic profiles.

ADAP-GC 4.0: Application of Clustering-Assisted Multivariate Curve Resolution to Spectral Deconvolution of Gas Chromatography-Mass Spectrometry Metabolomics Data

We report a multivariate curve resolution (MCR)-based spectral deconvolution workflow for untargeted gas chromatography-mass spectrometry metabolomics. As an essential step in preprocessing such data, spectral deconvolution computationally separates ions that are in the same mass spectrum but belong to coeluting compounds that are not resolved completely by chromatography. As a result of this computational separation, spectral deconvolution produces pure fragmentation mass spectra. Traditionally, spectral deconvolution has been achieved by using a model peak approach. We describe the fundamental differences between the model peak-based and the MCR-based spectral deconvolution and report ADAP-GC 4.0 that employs the latter approach while overcoming the associated computational complexity. ADAP-GC 4.0 has been evaluated using GC-TOF data sets from a 27-standards mixture at different dilutions and urine with the mixture spiked in, and GC Orbitrap data sets from mixtures of different standards. It produced the average matching scores 960, 959, and 926 respectively. Moreover, its performance has been compared against MS-DIAL, eRah, and ADAP-GC 3.2, and ADAP-GC 4.0 demonstrated a higher number of matched compounds and up to 6% increase of the average matching score.

Sampling methods for the study of volatile profile of PDO wine vinegars. A comparison using multivariate data analysis

High-quality wine vinegars have been registered in Spain under protected designation of origin (PDO): "Vinagre de Jerez", "Vinagre de Condado de Huelva" and "Vinagre de Montilla-Moriles". The raw material, production and aging processes determine their quality and their aromatic composition. Vinegar volatile profile is usually analyzed by gas chromatography-mass spectrometry (GC-MS), being necessary a previous extraction step. Thus, three different sampling methods (Headspace solid phase microextraction "HS-SPME", Headspace stir bar sorptive extraction "HSSE" and Dynamic headspace extraction "DHS") were studied for the analysis of the volatile composition of Spanish PDO wine vinegars. Multivariate curve resolution (MCR) was used to solve chromatographic problems, improving the results obtained. Principal component analysis (PCA) showed that not all the sampling methods were equally suitable for the characterization and differentiation between PDOs and categories, being HSSE the technique that made able the best vinegar characterization.

Chemometric methods in data processing of mass spectrometry-based metabolomics: A review

This review focuses on recent and potential advances in chemometric methods in relation to data processing in metabolomics, especially for data generated from mass spectrometric techniques. Metabolomics is gradually being regarded a valuable and promising biotechnology rather than an ambitious advancement. Herein, we outline significant developments in metabolomics, especially in the combination with modern chemical analysis techniques, and dedicated statistical, and chemometric data analytical strategies. Advanced skills in the preprocessing of raw data, identification of metabolites, variable selection, and modeling are illustrated. We believe that insights from these developments will help narrow the gap between the original dataset and current biological knowledge. We also discuss the limitations and perspectives of extracting information from high-throughput datasets.

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.

Optimization of wavelength range and data interval in chemometric analysis of complex pharmaceutical mixtures

The performance of different chemometric approaches was evaluated in the spectrophotometric determination of pharmaceutical mixtures characterized by having the amount of components with a very high ratio. Principal component regression (PCR), partial least squares with one dependent variable (PLS1) or multi-dependent variables (PLS2), and multivariate curve resolution (MCR) were applied to the spectral data of a ternary mixture containing paracetamol, sodium ascorbate and chlorpheniramine (150:140:1, m/m/m), and a quaternary mixture containing paracetamol, caffeine, phenylephrine and chlorpheniramine (125:6. 25:1.25:1, m/m/m/m). The UV spectra of the calibration samples in the range of 200–320 nm were pre-treated by removing noise and useless data, and the wavelength regions having the most useful analytical information were selected using the regression coefficients calculated in the multivariate modeling. All the defined chemometric models were validated on external sample sets and then applied to commercial pharmaceutical formulations. Different data intervals, fixed at 0.5, 1.0, and 2.0 point/nm, were tested to optimize the prediction ability of the models. The best results were obtained using the PLS1calibration models and the quantification of the species of a lower amount was significantly improved by adopting 0.5 data interval, which showed accuracy between 94.24% and 107.76%.

Quantitative analysis of 17 amino acids in tobacco leaves using an amino acid analyzer and chemometric resolution

A method was developed for quantifying 17 amino acids in tobacco leaves by using an A300 amino acid analyzer and chemometric resolution. In the method, amino acids were eluted by the buffer solution on an ion-exchange column. After reacting with ninhydrin, the derivatives of amino acids were detected by ultraviolet detection. Most amino acids are separated by the elution program. However, five peaks of the derivatives are still overlapping. A non-negative immune algorithm was employed to extract the profiles of the derivatives from the overlapping signals, and then peak areas were adopted for quantitative analysis of the amino acids. The method was validated by the determination of amino acids in tobacco leaves. The relative standard deviations (n = 5) are all less than 2.54% and the recoveries of the spiked samples are in a range of 94.62-108.21%. The feasibility of the method was proved by analyzing the 17 amino acids in 30 tobacco leaf samples.

Application of chemometrics to resolve overlapping mass spectral peak clusters between trichloroethylene and its deuterated internal standard

RATIONALE

Using one or two (2) H-atom-labeled analogs as internal standards (ISs) may cause a 'cross-contribution' problem (i.e., the overlap of ions from the IS and the analyte) especially for halogenated volatile organic compounds (VOCs). However, in this situation the overlapping peak clusters of the analyte and ISs can be resolved by multivariate chemometric methods such as classical least-squares (CLS) and inverse least-squares (ILS).

METHODS

Trichloroethylene (TCE) and its internal standard, deuterated TCE (TCE-d), as model compounds, were analyzed using portable gas chromatography/mass spectrometry. CLS and ILS were applied to resolve overlapping TCE and TCE-d mass spectral signals and evaluated for the determination of TCE. CLS and ILS models were constructed and used to predict concentration ratios of TCE to TCE-d. Calibration samples were prepared by adding TCE at different concentrations and TCE-d at 300 ng mL(-1) as an IS.

RESULTS

The calibration curve was linear over a range of 10-1000 ng mL(-1) with a coefficient of determination (R(2)) of 0.993. A validation data set collected 2 weeks later was used to further test the model robustness. Lower prediction errors and higher correlation coefficients were obtained from TCE/TCE-d ratios predicted by the CLS model.

CONCLUSIONS

This paper describes the first application of CLS to deconvolute overlapping peaks between an analyte and its corresponding isotopic internal standard for quantification. The proposed method enables simple isotopic analogs of analytes (one H or C atom is isotopically labeled) to be used as internal standards for analytes with isotopic distributions. It has wide application because of the environmental impact and prevalence of halogenated VOCs, especially when analytes have isotopic distributions that overlap with an internal standard or when sophisticated isotopic analogs of the analytes with three or more (2)H- or/and (13)C-atoms are prohibitively expensive or even impossible.

WITHDRAWN: Recent advances in chemometric methods for plant metabolomics: A review

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Multivariate curve resolution: a review of advanced and tailored applications and challenges

Multivariate curve resolution (MCR) is a widespread methodology for the analysis of process data in many different application fields. This article intends to propose a critical review of the recently published works. Particular attention will be paid to situations requiring advanced and tailored applications of multivariate curve resolution, dealing with improvements in preprocessing methods, multi-set data arrangements, tailored constraints, issues related to non-ideal noise structure and deviation to linearity. These analytical issues are tackling the limits of applicability of MCR methods and, therefore, they can be considered as the most challenging ones.