Interpretation of omics data analyses.

Omics studies attempt to extract meaningful messages from large-scale and high-dimensional data sets by treating the data sets as a whole. The concept of treating data sets as a whole is important in every step of the data-handling procedures: the pre-processing step of data records, the step of statistical analyses and machine learning, translation of the outputs into human natural perceptions, and acceptance of the messages with uncertainty. In the pre-processing, the method by which to control the data quality and batch effects are discussed. For the main analyses, the approaches are divided into two types and their basic concepts are discussed. The first type is the evaluation of many items individually, followed by interpretation of individual items in the context of multiple testing and combination. The second type is the extraction of fewer important aspects from the whole data records. The outputs of the main analyses are translated into natural languages with techniques, such as annotation and ontology. The other technique for making the outputs perceptible is visualization. At the end of this review, one of the most important issues in the interpretation of omics data analyses is discussed. Omics studies have a large amount of information in their data sets, and every approach reveals only a very restricted aspect of the whole data sets. The understandable messages from these studies have unavoidable uncertainty.

GC-FID-MS Based Metabolomics to Access Plum Brandy Quality.

Plum brandy (Slivovitz (en); Sljivovica(sr)) is an alcoholic beverage that is increasingly consumed all over the world. Its quality assessment has become of great importance. In our study, the main volatiles and aroma compounds of 108 non-aged plum brandies originating from three plum cultivars, and fermented using different conditions, were investigated. The chemical profiles obtained after two-step GC-FID-MS analysis were subjected to multivariate data analysis to reveal the peculiarity in different cultivars and fermentation process. Correlation of plum brandy chemical composition with its sensory characteristics obtained by expert commission was also performed. The utilization of PCA and OPLS-DA multivariate analysis methods on GC-FID-MS, enabled discrimination of brandy samples based on differences in plum varieties, pH of plum mash, and addition of selected yeast or enzymes during fermentation. The correlation of brandy GC-FID-MS profiles with their sensory properties was achieved by OPLS multivariate analysis. Proposed workflow confirmed the potential of GC-FID-MS in combination with multivariate data analysis that can be applied to assess the plum brandy quality.

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.

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.

Performance Evaluation of a Nontargeted Platform Using Two-Dimensional Gas Chromatography Time-of-Flight Mass Spectrometry Integrating Computer-Assisted Structure Identification and Automated Semiquantification for the Comprehensive Chemical Characterization of a Complex Matrix.

Nontargeted screening methodologies are powerful approaches for comprehensive chemical characterization of complex matrixes. In order to maximize chemical space coverage, three analytical methods using two-dimensional gas chromatography with time-of-flight mass spectrometry for nonpolar, polar, and volatile compounds have been established. The structural identification process was streamlined with an in-house developed computer-assisted structure identification platform, which facilitated the identification of novel compounds and also delivered semiquantitative concentrations for all compounds. Key performance parameters for this nontargeted platform, including chemical space coverage, confidence for structural identification, accuracy of semiquantification, and performance of differential analysis, were evaluated. The automated structural identification process was assessed using a subset of 243 compounds (out of 2990), which were confirmed to be present in cigarette smoke using reference standards. Consistently high true positive identification rates between 88.2% and 96.2% across the different concentration ranges investigated were demonstrated. Accuracy for semiquantification was assessed by comparison with quantitative data from literature, where a maximum 4-fold deviation from available targeted analysis values was estimated.

Application of different chromatographic techniques and chemometric analysis in authenticity testing of plant protection products containing azoxystrobin as an active substance.

Azoxystrobin (methyl(2E)-2-{2-[6-(2-cyanophenoxy)pyrimidin-4-yloxy] phenyl}-3-methoxyacrylate) is an active ingredient used to protect crops against fungal diseases. The experience of the Polish control laboratory indicates relatively frequent cases of counterfeit plant protection products (PPPs) containing this active substance. The present study aimed to use chemometric methods to model chemical fingerprints obtained by different chromatographic techniques to verify the original formulation of PPPs containing the active substance azoxystrobin. The pesticides used in the study came from different sources (including stores and warehouses), were manufactured at a different time and came from different production batches. The results obtained with the HPLC-DAD and HS-GC-MS techniques were then modeled using principal component analysis (PCA) and soft independent modeling by class analogy (SIMCA) classifier. The proposed approach has been confirmed as useful for verifying the authenticity of PPPs and can be used in the routine control testing of SC pesticides containing azoxystrobin.

Evolution of Volatile Compounds and Spoilage Bacteria in Smoked Bacon during Refrigeration Using an E-Nose and GC-MS Combined with Partial Least Squares Regression.

The changes in the concentration of volatile organic compounds (VOCs) and biogenic amines (BAs) in smoked bacon during 45-day refrigerated storage is investigated using solid-phase micro-extraction coupled with gas chromatography-mass spectrometry and high-performance liquid chromatography. In total, 56 VOCs and 6 BAs were identified and quantified. The possible pathways leading to their formation are analyzed and considered as the potential signs of microbial activity, especially by specific spoilage microorganisms (SSOs). Leuconostoc and Lactobacillus, which levels increased markedly with the extension of storage time, were recognized as SSOs. An electronic nose (e-nose) was employed to determine the changes in concentration of the odor components per sample present within half an hour. Partial least squares regression was then carried out to analyze the correlation between SSO growth, metabolite concentration, BA accumulation, and e-nose response. The results show that ten VOCs (ethanol, 2-furanmethanol, 1-hexanol, 1-propanol, phenol, 2-methoxyphenol, acetic acid, 3-ethyl-2-cyclopenten-1-one, furfural, and ethyl hexanoate) and three BAs (putrescine, cadaverine, and tyramine) can be associated with the growth of SSOs. Thus, they can be adopted as potential indicators to evaluate and monitor the quality of the bacon and develop appropriate detection methods. E-noses can used to recognize odors and diagnose quality of bacon.

Isotope cluster-based compound matching in gas chromatography/mass spectrometry for non-targeted metabolomics.

Gas chromatography coupled to mass spectrometry (GC/MS) has emerged as a powerful tool in metabolomics studies. A major bottleneck in current data analysis of GC/MS-based metabolomics studies is compound matching and identification, as current methods generate high rates of false positive and false-negative identifications. This is especially true for data sets containing a high amount of noise. In this work, a novel spectral similarity measure based on the specific fragmentation patterns of electron impact mass spectra is proposed. An important aspect of these algorithmic methods is the handling of noisy data. The performance of the proposed method compared to the dot product, the current gold standard, was evaluated on a complex biological data set. The analysis results showed significant improvements of the proposed method in compound matching and chromatogram alignment compared to the dot product.

Correction for isotopic interferences between analyte and internal standard in quantitative mass spectrometry by a nonlinear calibration function.

Stable isotope-labeled internal standards are of great utility in providing accurate quantitation in mass spectrometry (MS). An implicit assumption has been that there is no "cross talk" between signals of the internal standard and the target analyte. In some cases, however, naturally occurring isotopes of the analyte do contribute to the signal of the internal standard. This phenomenon becomes more pronounced for isotopically rich compounds, such as those containing sulfur, chlorine, or bromine, higher molecular weight compounds, and those at high analyte/internal standard concentration ratio. This can create nonlinear calibration behavior that may bias quantitative results. Here, we propose the use of a nonlinear but more accurate fitting of data for these situations that incorporates one or two constants determined experimentally for each analyte/internal standard combination and an adjustable calibration parameter. This fitting provides more accurate quantitation in MS-based assays where contributions from analyte to stable labeled internal standard signal exist. It can also correct for the reverse situation where an analyte is present in the internal standard as an impurity. The practical utility of this approach is described, and by using experimental data, the approach is compared to alternative fits.

PyMS: a Python toolkit for processing of gas chromatography-mass spectrometry (GC-MS) data. Application and comparative study of selected tools.

BACKGROUND: Gas chromatography-mass spectrometry (GC-MS) is a technique frequently used in targeted and non-targeted measurements of metabolites. Most existing software tools for processing of raw instrument GC-MS data tightly integrate data processing methods with graphical user interface facilitating interactive data processing. While interactive processing remains critically important in GC-MS applications, high-throughput studies increasingly dictate the need for command line tools, suitable for scripting of high-throughput, customized processing pipelines. RESULTS: PyMS comprises a library of functions for processing of instrument GC-MS data developed in Python. PyMS currently provides a complete set of GC-MS processing functions, including reading of standard data formats (ANDI- MS/NetCDF and JCAMP-DX), noise smoothing, baseline correction, peak detection, peak deconvolution, peak integration, and peak alignment by dynamic programming. A novel common ion single quantitation algorithm allows automated, accurate quantitation of GC-MS electron impact (EI) fragmentation spectra when a large number of experiments are being analyzed. PyMS implements parallel processing for by-row and by-column data processing tasks based on Message Passing Interface (MPI), allowing processing to scale on multiple CPUs in distributed computing environments. A set of specifically designed experiments was performed in-house and used to comparatively evaluate the performance of PyMS and three widely used software packages for GC-MS data processing (AMDIS, AnalyzerPro, and XCMS). CONCLUSIONS: PyMS is a novel software package for the processing of raw GC-MS data, particularly suitable for scripting of customized processing pipelines and for data processing in batch mode. PyMS provides limited graphical capabilities and can be used both for routine data processing and interactive/exploratory data analysis. In real-life GC-MS data processing scenarios PyMS performs as well or better than leading software packages. We demonstrate data processing scenarios simple to implement in PyMS, yet difficult to achieve with many conventional GC-MS data processing software. Automated sample processing and quantitation with PyMS can provide substantial time savings compared to more traditional interactive software systems that tightly integrate data processing with the graphical user interface.

Multivariate analysis of HT/GC-(IT)MS chromatographic profiles of triacylglycerol for classification of olive oil varieties.

The ability of multivariate analysis methods such as hierarchical cluster analysis, principal component analysis and partial least squares-discriminant analysis (PLS-DA) to achieve olive oil classification based on the olive fruit varieties from their triacylglycerols profile, have been investigated. The variations in the raw chromatographic data sets of 56 olive oil samples were studied by high-temperature gas chromatography with (ion trap) mass spectrometry detection. The olive oil samples were of four different categories ("extra-virgin olive oil", "virgin olive oil", "olive oil" and "olive-pomace" oil), and for the "extra-virgin" category, six different well-identified olive oil varieties ("hojiblanca", "manzanilla", "picual", "cornicabra", "arbequina" and "frantoio") and some blends of unidentified varieties. Moreover, by pre-processing methods of chemometric (to linearise the response of the variables) such as peak-shifting, baseline (weighted least squares) and mean centering, it was possible to improve the model and grouping between different varieties of olive oils. By using the first three principal components, it was possible to account for 79.50% of the information on the original data. The fitted PLS-DA model succeeded in classifying the samples. Correct classification rates were assessed by cross-validation.

Optimization of detection conditions and single-laboratory validation of a multiresidue method for the determination of 135 pesticides and 25 organic pollutants in grapes and wine by gas chromatography time-of-flight mass spectrometry.

This paper describes single-laboratory validation of a multiresidue method for the determination of 135 pesticides, 12 dioxin-like polychlorinated biphenyls, 12 polyaromatic hydrocarbons, and bisphenol A in grapes and wine by GC/time-of-flight MS in a total run time of 48 min. The method is based on extraction with ethyl acetate in a sample-to-solvent ratio of 1:1, followed by selective dispersive SPE cleanup for grapes and wine. The GC/MS conditions were optimized for the chromatographic separation and to achieve highest S/N for all 160 target analytes, including the temperature-sensitive compounds, like captan and captafol, that are prone to degradation during analysis. An average recovery of 80-120% with RSD < 10% could be attained for all analytes except 17, for which the average recoveries were 70-80%. LOQ ranged within 10-50 ng/g, with < 25% expanded uncertainties, for 155 compounds in grapes and 151 in wine. In the incurred grape and wine samples, the residues of buprofezin, chlorpyriphos, metalaxyl, and myclobutanil were detected, with an RSD of < 5% (n = 6); the results were statistically similar to previously reported validated methods.

Determination of polycyclic aromatic hydrocarbons in airborne particulate matter by gas chromatography-triple quadrupole tandem mass spectrometry.

An accurate and sensitive method for the identification and the quantification of 24 polycyclic aromatic hydrocarbons (PAHs) in airborne particulate matter has been developed and validated using GC/MS/MS with a triple quadrupole analyzer. Ultrasonic extraction (USE) and pressurized liquid extraction (PLE) were evaluated. PLE was selected because it provided better recoveries and reduced treatment time and use of solvents compared to USE. Cleanup of PLE extracts was not necessary. The accuracy of the optimized method was tested using a standard reference material of urban dust (SRM 1649a). PAH recoveries were between 70 and 99% (except for naphthalene, which was lower than 20%) with RSDs ranging from 2.6 to 15.3%. Linearity in the range 5-500 ng/mL provided R2 values higher than 0.99 for all compounds. LODs for PAHs ranged from 0.001 to 0.435 ng/mL and LOQs ranged from 0.003 to 0.757 ng/mL. The proposed method was applied to the analysis of real samples collected in the city of Almeria, Spain, where some PAHs were found at levels lower than the legislated limit.

Development of a gas chromatographic/mass spectrometric method for determination of phthalates in oily foods.

A GC/MS method was developed for simultaneous determination of 12 phthalates and four other plasticizers--acetyl tributyl citrate, di(2-ethylhexyl) adipate, dibutyl sebacate, di-isononyl cyclohexane 1,2-dicarboxylate (DINCH)--in vegetable oil, pesto, and tomato sauce. Samples were extracted with acetonitrile and cleaned on a Florisil column. The final extract was analyzed by GC in combination with ion trap MS. The phthalates and di-isononyl cyclohexane 1,2-dicarboxylate were detected by MS/MS, while the other three plasticizers were monitored in the same GC run using full scan mode. The analytical process was validated in each matrix by the analysis of blank samples. Performance characteristics, such as linearity, LOQ, precision, and recoveries were studied. Studies at fortification levels of 0.25-200 mg/kg gave mean recoveries ranging from 71 to 106% and RSD values between 7 and 12% for all compounds. LOQs were 0.050-0.10 mg/kg for all the target compounds except di-isononyl phthalate, di-isodecyl phthalate, and DINCH (2.0 mg/kg).

Simultaneous determination of eight sympathomimetic amines in urine by gas chromatography/mass spectrometry.

A GC method was developed for the identification and quantitation of eight sympathomimetic amines in urine, i.e., amphetamine, methamphetamine, mephentermine, ephedrine, pseudoephedrine, methylenedioxyamphetamine, methylenedioxymethamphetamine, and methylenedioxyethylamphetamine. Methoxyphenamine was used as the internal standard (IS). The assay is rapid, sensitive, and simple to perform. It involves a liquid-liquid extraction procedure with simultaneous in-solution derivatization of the organic layer with pentafluorobenzoyl chloride (PFB-CI), followed by GC/MS analysis. These derivatives and the IS were extracted from 1 mL alkaline urine into hexane before derivatization with PFB-CI. The organic layer was then removed and evaporated to dryness before dissolution with hexane for GC/MS analysis. Calibration curves for each analyte showed linearity in the range of 25-5000 ng/mL (r2 > or = 0.997). Recoveries ranged from 88 to 99%, with the precision of recoveries typically < or = 5%. The LOD values ranged from 7 to 28 ng/mL, and the LOQ values ranged from 23 to 94 ng/mL. At least four ions were available for each analyte for confirmation of identity by MS.

[Examination of Analytical Methods for Methanol, Trichloroethylene, and Tetrachloroethylene to Revise the Official Methods Based on the Act on the Control of Household Products Containing Harmful Substances].

In Japan, the use of methanol, trichloroethylene, and tetrachloroethylene in aerosol household products is banned under the Act on the Control of Household Products Containing Harmful Substances. As the official analytical methods for testing for these substances have not been revised for over 35 years, several issues have been pointed out. Thus, we developed a new method to revise the official method in our previous study. In this study, validation of the proposed method for detecting the target substances was conducted using two aerosol-product samples (A and B), which contained methanol, trichloroethylene, and tetrachloroethylene. Sample A comprised regulated values of these compounds, while sample B comprised one-tenth of the regulated amounts. They also contained several volatile compounds that served as interfering substances. Subsequently, the samples were analyzed using head space/gas chromatography-mass spectrometry, and it was confirmed that the three target substances were separated from the other chemicals on chromatograms. Validation tests were conducted at seven laboratories to evaluate the proposed method using the prepared samples. In one laboratory, the recovery of trichloroethylene and tetrachloroethylene in sample B was slightly higher at 120%, while the recoveries obtained from the other tests were between 70% and 120%. Relative standard deviation at each laboratory was less than 10%. Furthermore, the relative standard deviations between the validation tests with respect to each chemical were less than 15%. Therefore, the method validated in this study was considered to be effective as a revised method for testing for methanol, trichloroethylene, and trichloroethylene in household aerosol products.

Unraveling the chemosensory characteristics of strong-aroma type Baijiu from different regions using comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry and descriptive sensory analysis.

Comprehensive 2D gas chromatography-time-of-flight mass spectrometry was combined with descriptive sensory analysis to elucidate the specificity of strong-aroma type Baijiu (Chinese liquor) from different regions, based on regionally distinct flavor characterized by chemical and sensory profiles. Numerous potential aroma compounds (262) were identified, among which 58 aroma compounds were significantly different between the samples from Sichuan and Jianghuai regions. Relationships between these potential aroma compounds and sensory attributes were investigated by partial least squares regression and network analysis. The compounds that dominantly contributed to the important sensory attributes were identified. The high pyrazines, furanoids, and carbonyls amounts contributed to the high intensities of the cellar, toasted, and grain aroma profiles of the Sichuan region samples, while the high ester and alcohol levels contributed to the fruity and floral aroma profiles of the Jianghuai region samples. This approach may have practical application in flavor characterization of other alcoholic beverages.

Untargeted and targeted metabolomics strategy for the classification of strong aroma-type baijiu (liquor) according to geographical origin using comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry.

A metabolomics strategy was developed to differentiate strong aroma-type baijiu (SAB) (distilled liquor) from the Sichuan basin (SCB) and Yangtze-Huaihe River Basin (YHRB) through liquid-liquid extraction coupled with GC×GC-TOFMS. PCA effectively separated the samples from these two regions. The PLS-DA training model was excellent, with explained variation and predictive capability values of 0.988 and 0.982, respectively. As a result, the model demonstrated its ability to perfectly differentiate all the unknown SAB samples. Twenty-nine potential markers were located by variable importance in projection values, and twenty-four of them were identified and quantitated. Discrimination ability is closely correlated to the characteristic flavor compounds, such as acid, esters, furans, alcohols, sulfides and pyrazine. Most of the marker compounds were less abundant in the SCB samples than in the YHRB samples. The quantitated markers were further processed using hierarchical cluster analysis for targeted analysis, indicating that the markers had great discrimination power to differentiate the SAB samples.

Chemical typicality of South American red wines classified according to their volatile and phenolic compounds using multivariate analysis.

In this study, 83 wines representating four commercial categories: "Argentinean Malbec", "Brazilian Merlot", "Uruguayan Tannat" and "Chilean Carménère" were analyzed according to their phenolic and volatile compounds. The objective was to identify the chemical compounds that would typify each category. From approximately about 600 peaks obtained by chromatographic techniques, 169 were identified and 53 of them were selected for multivariate statistical analysis. Chilean Carménère was the best discriminated group by the methods applied in our study, followed by Argentinean Malbec. Brazilian Merlot mixed mainly with some Carménère, whileTannat mixed with all wines categories, especially Malbec. In general, Chilean Carménère wines can be characterized by a bluish color, higher amounts of sulphur dioxide, higher content of octanoic acid, isobutanol, ethyl isoamyl succinate and catechin and a smaller amount of quercetin. These data can contribute for further process of authenticity or typification of South American red wines.

Targeted toxicometabolomics of endosulfan sulfate in adult zebrafish (Danio rerio) using GC-MS/MS in multiple reaction monitoring mode.

Endosulfan sulfate is a major oxidative metabolite of the chlorinated insecticide endosulfan. In this study, a targeted metabolomics approach was used to investigate the toxic mechanisms of endosulfan sulfate in adult zebrafish using the multiple reaction monitoring mode of a GC-MS/MS. The LC50 of endosulfan sulfate in adult zebrafish was determined and then zebrafish were exposed to endosulfan sulfate at one-tenth the LC50 (0.1LC50) or the LC50 for 24 and 48 h. After exposure, the fish were extracted, derivatized and analyzed by GC-MS/MS for 379 metabolites to identify 170 metabolites. Three experimental groups (control, 0.1LC50 and LC50) were clearly separated in PLS-DA score plots. Based on the VIP, ANOVA, and fold change results, 40 metabolites were selected as biomarkers. Metabolic pathways associated with those metabolites were identified using MetaboAnalyst 4.0 as follows: aminoacyl-tRNA biosynthesis, valine/leucine/isoleucine biosynthesis, citrate cycle, glycerolipid metabolism, and arginine/proline metabolism. Gene expression studies confirmed the activation of citrate cycle and glycerolipids metabolism. MDA levels of the exposed group significantly increased in oxidative toxicity assay tests. Such significant perturbations of important metabolites within key biochemical pathways must result in biologically hazardous effects in zebrafish.