Gas Chromatography-mass Spectrometry Data 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.

Accurate classification of the molecular characteristics of soluble portions from various lignites: Joint analysis of thermal dissolution experiments and data mining methods

Gas chromatography-mass spectrometry (GC-MS) was used to analyze the soluble portions (SPs) of eight lignites which were obtained by extracting using cyclohexane, methanol and ethanol, respectively, as the solvent. For thermal dissolution at 300 °C using cyclohexane, the intermolecular interactions between mobile phase and macromolecules in coal were weakened. Consequently, non- and low-polar compounds such as alkanes and aromatics were well dissolved. Methanol and ethanol were more likely to closely interact with oxygen-containing functional groups and promoted the breakage of hydrogen bonds and weak covalent bonds. Hierarchical cluster analysis classified all the 24 SPs, and the SPs from cyclohexane and ethanol solvents exhibited more similarity. Partial least squares discriminant analysis was established to reproduce the classification results of SPs entirely and accurately. The SPs were further classified by combining a small number of geochemical characteristic compounds rather than massive GC-MS data.

Electron ionization mass spectrometry feature peak relationships combined with deep classification model to assist similarity algorithm for fast and accurate identification of compounds.

Gas chromatography-mass spectrometry (GC-MS) combines chromatography and MS, providing full play to the advantages of high separation efficiency of GC, strong qualitative ability of MS, and high sensitivity of detector. In GC-MS data processing, determining the experimental compounds is one of the most important analytical steps, which is usually realized by one-to-one similarity calculations between the experimental mass spectrum and the standard mass spectrum library. Although the accuracy of the algorithm has been improved in recent years, it is still difficult to distinguish structurally similar mass spectra, especially isomers. At the same time, the library capacity is very large and increasing every year, and the algorithm needs to perform large numbers of calculations with irrelevant compounds in the library to recognize unknown compounds, which leads to a significant reduction in efficiency. This work proposed to exclude a large number of irrelevant mass spectra by presearching, perform preliminary similarity calculations using similarity algorithms, and finally improve the accuracy of similarity calculations using deep classification models. The replica library of NIST17 is used as the query data, and the master library is used as the reference database. Compared with the traditional recognition algorithm, the preprocessing algorithm has reduced the time by 4.2h, and by adding the deep learning models 1 and 2 as the final determination, the recognition accuracy has been improved by 1.9% and 6.5%, respectively, based on the original algorithm. This method improves the recognition efficiency compared to conventional algorithms and at the same time has better recognition accuracy for structurally similar mass spectra and isomers.

Biomarker Aroma Compounds of Monofloral Honey From Kazakhstan by Gas Chromatography–Mass Spectrometry (GC–MS) and Chemometric Analysis*

The aroma compounds of honey provide useful information to determine the geographical origin. This work focuses on the study of aroma compounds of 16 honey samples using headspace (HS)-solid-phase microextraction (SPME) gas chromatography–mass spectrometry (GC-MS) to reaffirm the geographical features of 4 monofloral honey. 7 sunflower, 3 buckwheat, 2 agrimonia, and 4 tamarix monofloral honey samples were obtained from four regions of Kazakhstan. Moreover, the GC-MS data were applied to chemometric analysis for the common aroma compounds to discriminate the monofloral honeys. In total, 76 aroma compounds were detected and 72 were identified. α-Pinene, benzaldehyde, octanal, hotrienol, limonene, cosmene, nonanol, linalool oxide, dehydro-p-cymene, β-linalool, 2-nonen-1-ol, carvacrol, lilac aldehyde A, β-citral, 2-decen-1-al, and β-damascenone were identified as common aroma compounds for all honey types. According to principal component analysis (PCA), the aroma compounds of sunflower, buckwheat, agrimonia, and tamarix honey were distinguished. Dehydro-p-cymene, β-linalool, 2-nonen-1-ol, and linalool oxide were determined as dominant compounds for agrimonia honey, while nonanol, benzaldehyde, octanol, dehydro-p-cymene and β-linalool for buckwheat, β-citral, lilac aldehyde A, 2-decen-1-al, D-limonene, β-damascenone, linalool oxide for tamarix honey, and hotrienol, cosmene, α-pinene, and carvacrol for the sunflower product. PCA revealed that the dominant compounds for each honey could be used as biomarker compounds to detect the origin of the honey and possible adulteration, such as blending and inverted sugar addition.

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.

Acute Toxicity Study of Hexane Extract of <i>Sodhita Semecarpus anacardium</i> L. Drupe in Wistar Albino Rats and its Prediction Using <i>In-Silico</i> Tool

Semecarpus anacardium L. belongs to the family Anacardiaceae. Its drupe is widely used in Ayurvedic formulations after sodhana processing as it is listed under Schedule E1 of the Drugs and Cosmetics Act 1940. In this study, sodhita Semecarpus anacardium L. drupe was extracted with n-hexane and the yield was found to be 56%. The extract was subjected to GCMS (Gas Chromatography Mass Spectrometry) analysis and its characteristics were studied using an in silico tool. Acute oral and dermal toxicity studies were performed on the Wistar albino rats as per the OECD 425 and 402 guidelines respectively. The GCMS data revealed the presence of C13H18O3 (m/z: 222.1) with an abundance of 93.63%. The compound was predicted as potentially hazardous with a probable mutagenic category of ICH M7 class 3. An oxirane functional group of the compound was predicted to cause potent irritation properties to the eyes and skin with positive scores of 0.76 and 0.57 respectively. The LD50 was found to be more than the limit dose of 2000mg/kg body weight upon oral administration. Acute dermal exposure at a limited dose of 2000mg/kg body weight did not cause mortality. However, inflammatory responses started appearing within 48 hours of exposure. Histopathology revealed mild dermal oedema, mild fibrosis, dilated follicles hyperplasia etc. No changes were found in haematological parameters. Inflammation and dermal allergic reactions were completely self-healed by the end of 14 days. The results suggest that the oil portion of the sodhita drupe possesses the irritant properties of the Semecarpus anacardium. It might be due to the presence of the compound C13H18O3 with Oxirane as a functional group.

Formation of Furan from Linoleic Acid Thermal Oxidation: (E,E)-2,4-Decadienal as a Critical Intermediate Product.

The linoleic acid reaction models were set at 150 °C for 120 min, and its oxidation process was monitored by nuclear magnetic resonance (NMR) and gas chromatography-mass spectrometry (GC-MS). Results showed that no furan was formed from linoleic acid without heating, while furan accumulated throughout the heating process. Linoleic acid ran out within 30 min, which indicated that furan was formed mainly from the intermediate oxidation products of linoleic acid after 30 min. It should be noticed that the content of (E,E)-2,4-decadienal reached maximum once the linoleic acid ran out and then decreased with the formation of furan. Multivariate statistical analysis suggested that (E,E)-2,4-decadienal was the most important aldehyde related to furan formation during linoleic acid oxidation. To prove this assumption, the variation of furan from (E,E)-2,4-decadienal reaction models heating at 150 °C for 60 min was also studied. Results showed that the content of furan increased with the oxidation of (E,E)-2,4-decadienal. Furthermore, NMR and GC-MS data proved that (E,E)-2,4-decadienal could be oxidized to 4,5-epoxy-(E)-2-decenal. In conclusion, our results supported (E,E)-2,4-decadienal and trans-4,5-epoxy-(E)-2-decenal as critical intermediate products of furan formation from linoleic acid oxidation.

Analysis of differences in aroma and sensory characteristics of the mainstream smoke of six cigars

Cigars have unique aroma and style characteristics. In order to clarify the differences of aroma components between domestic and imported cigars and the material basis of the stylistic characteristics of different cigars, gas chromatography-mass spectrometry (GC-MS) and sensory evaluation were used to compare and analyze the aroma components in the mainstream smoke of four domestic cigars and two imported cigars. The GC-MS results showed that a total of 97 aroma components were measured in the smoke of the six cigars, and the types of aroma components were similar, but there were differences in their contents. In comparison with those of domestic cigars, imported cigars had suitable nicotine content, and higher contents of phytol, neophytadiene, 3-methylpentanoic acid, and (+)-δ-cadinene. To further explore the differences in the aroma components of the six cigars, GC-MS data combined with chemometrics were used to screen out 14 key aroma components based on P-value (P) < 0.05, Variable Importance Projection (VIP) > 1, and Aroma Activity Values (OAV) > 1. The key aroma components of each cigar were obtained, Snow Dream No. 5: cedrol; Wangguan Guocui: 6-methyl-5-hepten-2-one, pyridine, 2-ethyl-6-methylpyrazine; General Achileus No. 3: p-cresol, 2-methylbutyraldehyde, methyl cyclopentenolone; Montecristo No. 4: cedrol, 2-methylbutyraldehyde, guaiacol, 4-vinylguaiacol, methyl cyclopentenolone; Romeo y Julieta Wide Churchills: cedrol, 2,6-dimethylpyrazine, 2-ethyl-6-methylpyrazine, 2-heptanone, phenethyl alcohol; Great Wall No. 2: p-cresol, phenethyl alcohol, geranylacetone, methyl cyclopentenolone, dihydroactinidiolide. The odor descriptors of these compounds were consistent with the aroma profiles that were prominent in the senses of each cigar. This experiment initially explored the differences in aroma composition and style characteristics of cigars and provided data to support the quality improvement of domestic cigars.

A novel comprehensive strategy for high-thoroughly studying released compounds during the combustion process of herbs. A case study for Artemisia argyi Levl. et Vant

The comprehensive study of compound variations in released smoke during the combustion process is a great challenge in many scientific fields related to analytical chemistry like traditional Chinese medicine, environment analysis, food analysis, etc. In this work, we propose a new comprehensive strategy for efficiently and high-thoroughly characterizing compounds in the online released complex smokes: (i) A smoke capture device was designed for efficiently collecting chemical constituents to perform gas chromatography-mass spectrometry (GC-MS) based untargeted analysis. (ii) An advanced data analysis tool, AntDAS-GCMS, was used for automatically extracting compounds in the original acquired GC-MS data files. Additionally, a GC-MS data analysis guided instrumental parameter optimizing strategy was proposed for the optimization of parameters in the smoke capture device. The developed strategy was demonstrated by the study of compound variations in the smoke of traditional Chinese medicine, Artemisia argyi Levl. et Vant. The results indicated that more than 590 components showed significant differences among released smokes of various moxa velvet ratios. Finally, about 88 compounds were identified, of which phenolic compounds were the most abundant, followed by aromatics, alkenes, alcohols and furans. In conclusion, we may provide a novel approach to the studies of compounds in online released smoke.

Apoptosis-inducing anti-proliferative and quantitative phytochemical profiling with in silico study of antioxidant-rich Leea aequata L. leaves

Natural products have been important parts of traditional medicine since ancient times, with various promising health effects. Leea aequata (L. aequata), a natural product, has been widely used for treating several diseases due to its promising pharmacological activities. Therefore, the present study aimed to explore the phytochemical profiling and molecular docking of the antioxidant-rich part of L. aequata leaves and its antiproliferative activity. L. aequata leaves were extracted with methanol, followed by fractionation with the respective solvents to obtain the petroleum ether, chloroform, ethyl acetate, and aqueous fractions. The antioxidant activity was evaluated by spectrophotometric methods. The cytotoxic and antiproliferative activities were detected using MTT colorimetric and confocal microscopy methods, respectively. Phytochemical compositions were analyzed using gas chromatography‒mass spectrometry analysis. Computer aided (molecular docking SwissADME, AdmetSAR and pass prediction) analyses were undertaken to sort out the best-fit phytochemicals present in the plant responsible for antioxidant and anticancer effects. Among the fractions, the ethyl acetate fraction was the most abundant polyphenol-rich fraction and showed the highest antioxidant, reducing power, and free radical scavenging activities. Compared to untreated MCF-7 cells, ethyl acetate fraction-treated MCF-7 cells showed an increase in apoptotic characteristics, such as membrane blebbing, chromatin condensation, and nuclear fragmentation, causing apoptosis and decreased proliferation of HeLa and MCF-7 cells. Furthermore, gas chromatography mass spectrometry data revealed that the ethyl acetate fraction contained 16 compounds, including methyl esters of long-chain fatty acids, which are the major chemical constituents. Moreover, hexadecanoic acid, methyl ester; 9-octadecenoic acid (Z)-, methyl ester; 9,12-octadecadienoic acid, methyl ester (Z, Z) and phenol, 2,4-bis(1,1-dimethylethyl) are known to have antioxidant and cytotoxic activity, as confirmed by computer-aided models. A strong correlation was observed between the antioxidant and polyphenolic contents and the anticancer activity. In conclusion, we explored the possibility that L. aequata could be a promising source of antioxidants and anticancer agents with a high phytochemical profile.

Optimization of thermal steam treatment technology applied to Strelovskoye field using aquathermolysis catalysts

This study investigates the impact of process conditions, specifically temperature, pressure, and duration, on the composition and properties of high-viscosity oil extracted from the Strelovskoye field in the Samara region. The focus is on catalytic aquathermolysis and its potential for upgrading heavy oil. Laboratory simulations of the thermal steam treatment (TST) process were conducted with and without a catalyst, varying the time intervals (24–96 h) and treatment temperatures (250 and 300 °C).The synthesized iron-based catalyst precursor's efficiency in improving the quality of high-viscosity oil was evaluated using various physico-chemical research methods. The analysis revealed notable changes in the gas composition of the aquathermolysis products. Hydrogen sulfide content decreased, carbon dioxide content increased, and normal alkanes showed higher levels. Remarkably, the dynamic viscosity of the oil significantly decreased after TST at 250 °C, reaching a sixfold reduction compared to the initial sample. Similarly, TST at 300 °C resulted in an eightfold reduction compared to the initial sample and nearly a 4.5-fold reduction compared to the control sample after 96 h of treatment. Additionally, gas chromatography-mass spectrometry (GC–MS) data indicated intensified cracking reactions of C-C bonds, leading to the formation of lighter products. These findings highlight the potential advantages of catalytic aquathermolysis in the upgrading of heavy oil, underscoring its ability to modify the composition and properties of high-viscosity oil extracted from the Strelovskoye field.

Novel breath biomarkers identification for early detection of hepatocellular carcinoma and cirrhosis using ML tools and GCMS.

According to WHO 2019, Hepatocellular carcinoma (HCC) is the fourth highest cause of cancer death worldwide. More precise diagnostic models are needed to enhance early HCC and cirrhosis quick diagnosis, treatment, and survival. Breath biomarkers known as volatile organic compounds (VOCs) in exhaled air can be used to make rapid, precise, and painless diagnoses. Gas chromatography and mass spectrometry (GCMS) are utilized to diagnose HCC and cirrhosis VOCs. In this investigation, metabolically generated VOCs in breath samples (n = 35) of HCC, (n = 35) cirrhotic, and (n = 30) controls were detected via GCMS and SPME. Moreover, this study also aims to identify diagnostic VOCs for distinction among HCC and cirrhosis liver conditions, which are most closely related, and cause misleading during diagnosis. However, using gas chromatography-mass spectrometry (GC-MS) to quantify volatile organic compounds (VOCs) is time-consuming and error-prone since it requires an expert. To verify GC-MS data analysis, we present an in-house R-based array of machine learning models that applies deep learning pattern recognition to automatically discover VOCs from raw data, without human intervention. All-machine learning diagnostic model offers 80% sensitivity, 90% specificity, and 95% accuracy, with an AUC of 0.9586. Our results demonstrated the validity and utility of GCMS-SMPE in combination with innovative ML models for early detection of HCC and cirrhosis-specific VOCs considered as potential diagnostic breath biomarkers and showed differentiation among HCC and cirrhosis. With these useful insights, we can build handheld e-nose sensors to detect HCC and cirrhosis through breath analysis and this unique approach can help in diagnosis by reducing integration time and costs without compromising accuracy or consistency.

Separation of an Industrial Mixture of Decalin or Naphthalene Fluorination Products: Cis-Perfluorodecalin, Trans-Perfluorodecalin and Perfluoro(butylcyclohexane): Physicochemical, Thermophysical, and Spectral Data

New physicochemical data for trans-perfluorodecalin (trans-PDF) and cis-perfluorodecalin (cis-PFD) are presented. Based on the differential scanning calorimetry, the temperature and heat of the solid−liquid phase transition are determined. The coefficients of Antoine’s equation are calculated based on the experimental temperature–pressure dependence data. This article also presents data on the rheological properties («zero» shear viscosity and apparent activation energy for the viscous flow) of the studied compounds. The dependencies of refractive index and excess volume (density) on temperature are studied. Gas chromatography–mass spectrometry data and FTIR, 13C NMR, and 19F NMR spectra are provided. The dependencies are given for the perfluoro(butylcyclohexane) (BCH)–trans-PFD, BCH–cis-PFD, and trans-PFD–cis-PFD binary systems and BCH–trans-PFD–cis-PFD ternary system: refractive index and density (liquid molar volume and excess molar volume) of composition and temperature. The dependences of the excess molar volume on the composition and temperature of the mixtures are correlated with Redlich-Kister and Kohler equations.

Metabolic profiles of peanut (Arachis hypogaea L.) in response to Puccinia arachidis fungal infection

Background Puccinia arachidis fungus causes rust disease in the peanut plants (Arachis hypogaea L.), which leads to high yield loss. Metabolomic profiling of Arachis hypogaea was performed to identify the pathogen-induced production of metabolites involved in the defense mechanism of peanut plants. In this study, two peanut genotypes, one susceptible (JL-24) and one resistant (GPBD-4) were inoculated with Puccinia arachidis fungal pathogen. The metabolic response was assessed at the control stage (0 day without inoculation), 2 DAI (Day after inoculation), 4 DAI and 6 DAI by Gas Chromatography-Mass Spectrometry (GC-MS). Results About 61 metabolites were identified by NIST library, comprising sugars, phenols, fatty acids, carboxylic acids and sugar alcohols. Sugars and fatty acids were predominant in leaf extracts compared to other metabolites. Concentration of different metabolites such as salicylic acid, mannitol, flavonoid, 9,12-octadecadienoic acid, linolenic acid and glucopyranoside were higher in resistant genotype than in susceptible genotype during infection. Systemic acquired resistance (SAR) and hypersensitive reaction (HR) components such as oxalic acid was elevated in resistant genotype during pathogen infection. Partial least square-discriminant analysis (PLS-DA) was applied to GC-MS data for revealing metabolites profile between resistant and susceptible genotype during infection. Conclusion The phenol content and oxidative enzyme activity i.e. catalase, peroxidase and polyphenol oxidase were found to be very high at 4 DAI in resistant genotype (p-value < 0.01). This metabolic approach provides information about bioactive plant metabolites and their application in crop protection and marker-assisted plant breeding.

Region of interest selection for GC×GC–MS data using a pseudo fisher ratio moving window with connected components segmentation

Comprehensive two-dimensional gas chromatography mass spectrometry (GC×GC–MS) data present several challenges for analysis largely because chemical factors drift along the chromatographic modes across different chromatographic runs, and there is frequently a lack of reliable molecular ion measurements with which to align data across multiple samples. Tensor decomposition techniques such as Parallel Factor Analysis (PARAFAC2/PARAFAC2×N) allow analysts to deconvolve closely eluting signals for quantitative and qualitative purposes. These techniques make relatively few assumptions about chromatographic peak shapes or the relative abundance of noise and allow for highly accurate representations of the underlying chemical phenomena using well-characterized and scrutinized principles of chemometrics. However, expert intervention and supervision is required to select appropriate Regions of Interest (ROI) and numbers of chemical components present in each ROI. We previously reported an automated ROI selection algorithm for GC–MS data in Giebelhaus et al. where we observed the ratio of the first and second eigenvalues within a moving window across the entire chromatogram. Here, we present an extension of this work to automatically detect ROIs in GC×GC–MS chromatograms, while making no assumptions about peak shape. First, we calculate the probabilities of each acquisition being in a ROI, then apply connected components segmentation to discretize the regions of interest. For sparse chromatograms we found the algorithm detected spurious peaks. To address this, we implemented an iterative ROI selection process where we autoscaled the moving window to the standard deviation of the noise from the previous iteration. Using three user-defined parameters, we generated informative ROIs on a wide range of GC×GC-TOFMS chromatograms.

Comparison of Metabolites and Species Classification of Thirteen Zingiberaceae Spices Based on GC-MS and Multi-Spectral Fusion Technology.

Due to a similar plant morphology in the majority of Zingiberaceae spices, substitution and adulteration frequently take place during the sales process. Therefore, it is important to analyze the metabolites and species classification of different Zingiberaceae spices. This study preliminarily explored the differences in the metabolites in thirteen Zingiberaceae spices through untargeted gas chromatography-mass spectrometry (GC-MS) and combined spectroscopy, establishing models for classifying different Zingiberaceae spices. On one hand, a total of 81 metabolites were successfully identified by GC-MS. Thirty-seven differential metabolites were screened using variable important in projection (VIP ≥ 1). However, the orthogonal partial least squares discriminant analysis (OPLS-DA) model established using GC-MS data only explained about 30% of the variation. On the other hand, the partial least squares discriminant analysis (PLS-DA) models with three spectral data fusion strategies were compared, and their classification accuracy reached 100%. Among them, the mid-level data fusion model based on latent variables had the best performance. This study provides a powerful tool for distinguishing different Zingiberaceae spices and assists in reducing the occurrence of substitution and adulteration phenomena.

Comparison of decision tree and naïve Bayes algorithms in detecting trace residue of gasoline based on gas chromatography-mass spectrometry data.

Fire debris analysis aims to detect and identify any ignitable liquid residues in burnt residues collected at a fire scene. Typically, the burnt residues are analysed using gas chromatography-mass spectrometry (GC-MS) and are manually interpreted. The interpretation process can be laborious due to the complexity and high dimensionality of the GC-MS data. Therefore, this study aims to compare the potential of classification and regression tree (CART) and naïve Bayes (NB) algorithms in analysing the pixel-level GC-MS data of fire debris. The data comprise 14 positive (i.e. fire debris with traces of gasoline) and 24 negative (i.e. fire debris without traces of gasoline) samples. The differences between the positive and negative samples were first inspected based on the mean chromatograms and scores plots of the principal component analysis technique. Then, CART and NB algorithms were independently applied to the GC-MS data. Stratified random resampling was applied to prepare three sets of 200 pairs of training and testing samples (i.e. split ratio of 7:3, 8:2, and 9:1) for estimating the prediction accuracies. Although both the positive and negative samples were hardly differentiated based on the mean chromatograms and scores plots of principal component analysis, the respective NB and CART predictive models produced satisfactory performances with the normalized GC-MS data, i.e. majority achieved prediction accuracy >70%. NB consistently outperformed CART based on the prediction accuracies of testing samples and the corresponding risk of overfitting except when evaluated using only 10% of samples. The accuracy of CART was found to be inversely proportional to the number of testing samples; meanwhile, NB demonstrated rather consistent performances across the three split ratios. In conclusion, NB seems to be much better than CART based on the robustness against the number of testing samples and the consistent lower risk of overfitting.

The Determination of the Spatial Distribution of Indigenous Lipid Biomarkers in an Immature Jurassic Sediment Using Time-of-Flight-Secondary Ion Mass Spectrometry.

The ability to detect and map lipids, including potential lipid biomarkers, within a sedimentary matrix using mass spectrometry (MS) imaging may be critical to determine whether potential lipids detected in samples returned from Mars are indigenous to Mars or are contaminants. Here, we use gas chromatography-mass spectrometry (GC-MS) and time-of-flight-secondary ion mass spectrometry (ToF-SIMS) datasets collected from an organic-rich, thermally immature Jurassic geologic sample to constrain MS imaging analysis of indigenous lipid biomarkers in geologic samples. GC-MS data show that the extractable fractions are dominated by C27-C30 steranes and sterenes as well as isorenieratene derivatives. ToF-SIMS spectra from organic matter-rich laminae contain a strong, spatially restricted signal for ions m/z 370.3, m/z 372.3, and m/z 386.3, which we assign to C27 sterenes, cholestane (C27), and 4- or 24-methyl steranes (C28), respectively, as well as characteristic fragment ions of isorenieratene derivatives, including m/z 133.1, m/z 171.1, and m/z 237.1. We observed individual steroid spatial heterogeneity at the scale of tens to hundreds of microns. The fine-scale heterogeneity observed implies that indigenous lipid biomarkers concentrated within specific regions may be detectable via ToF-SIMS in samples with even low amounts of organic carbon, including in samples returned from Mars.

Quantitative and classification analysis of red wine by infrared spectra and gas chromatography–mass spectrometry data coupling with a new variable selection method

Quantitative and classification analysis of red wine by infrared spectra and gas chromatography–mass spectrometry data coupling with a new variable selection method

Rapid Identification of Aphid Species by Headspace GC-MS and Discriminant Analysis.

Aphids are a ubiquitous group of pests in agriculture that cause serious losses. For sustainable aphid identification, it is necessary to develop a precise and fast aphid identification tool. A new simple chemotaxonomy approach to rapidly identify aphids was implemented. The method was calibrated in comparison to the established phylogenetic analysis. For chemotaxonomic analysis, aphids were crushed, their headspace compounds were collected through closed-loop stripping (CLS) and analysed using gas chromatography-mass spectrometry (GC-MS). GC-MS data were then subjected to a discriminant analysis using CAP12.exe software, which identified key biomarkers that distinguish aphid species. A dichotomous key taking into account the presence and absence of a set of species-specific biomarkers was derived from the discriminant analysis which enabled rapid and reliable identification of aphid species. As the method overcomes the limits of morphological identification, it works with aphids at all life stages and in both genders. Thus, our method enables entomologists to assign aphids to growth stages and identify the life history of the investigated aphids, i.e., the food plant(s) they fed on. Our experiments clearly showed that the method could be used as a software to automatically identify aphids.