Analysis Of 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.

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.

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.

Fast and automated biomarker detection in breath samples with machine learning

Volatile organic compounds (VOCs) in human breath can reveal a large spectrum of health conditions and can be used for fast, accurate and non-invasive diagnostics. Gas chromatography-mass spectrometry (GC-MS) is used to measure VOCs, but its application is limited by expert-driven data analysis that is time-consuming, subjective and may introduce errors. We propose a machine learning-based system to perform GC-MS data analysis that exploits deep learning pattern recognition ability to learn and automatically detect VOCs directly from raw data, thus bypassing expert-led processing. We evaluate this new approach on clinical samples and with four types of convolutional neural networks (CNNs): VGG16, VGG-like, densely connected and residual CNNs. The proposed machine learning methods showed to outperform the expert-led analysis by detecting a significantly higher number of VOCs in just a fraction of time while maintaining high specificity. These results suggest that the proposed novel approach can help the large-scale deployment of breath-based diagnosis by reducing time and cost, and increasing accuracy and consistency.

THE COMPARISON OF GC-MS DATA OF LEAVES OF ALOE VERA (A. BARBADENSIS MILL) PLANT GROWN IN DIFFERENT SOIL COMBINATIONS WITH CHEMICAL LAB WASTES

Purpose of the study: The main purpose of this study is to do the comparison of the chemical constituents present in the leaves of Aloe Vera (A. barbadensis Mill) plant grown in different soil combinations using Gas Chromatography- Mass Spectrometry (GC-MS) data analysis. Plants were grown in two different combinations of soil, one in normal soil and the other in the combination of normal soil and solid chemical waste obtained from the chemistry laboratory of an educational institute.
 Methodology: One Aloe Vera (A. barbadensis Mill) plant was grown in normal soil without adding any compost or any fertilizer to the soil. The other plant was grown in soil, which was combined with the solid chemical waste collected from the chemistry laboratory. After observing the morphological characters of the plants, plants were studied for the chemical constituents present in them by using GC-MS data analysis technique. 
 Main Findings: Growth of plants depends on the soil composition, physical characters and the surrounding environment. How the variation in chemical composition of soil affects the chemical constituents of plant leaves, has been discussed in this paper. According to morphological characters the Aloe Vera (A. barbadensis Mill) plant grown with soil combined with solid chemical waste has shown better results compared with the one grown in normal soil. GC-MS results also indicated variation in the chemical constitution of plant leaves taken for the research experiment. 
 Applications of this study: This study has helped to understand that the soil environment and soil nutrients are largely responsible for the changes in chemical constituents of plants. This study can be applied to the other plants as well.
 Novelty/Originality of this study: In place of fertilizers, solid chemical waste from the laboratory was used for the research purpose. The method is useful and if implemented on a large scale, will help to curb pollution caused by educational institutes to some extent. This kind of research is not done previously by any other researcher.

A New Receptor Model Based on the Alternating Trilinear Decomposition Followed by a Score Matrix Reconstruction for Source Apportionment of Ambient Particulate Matter

A new receptor model based on the alternating trilinear decomposition followed by a score matrix reconstruction (ATLD-SMR) was developed for the source apportionment of urban PM10 for the first time. First, simulated three-way data arrays of gas chromatography-mass spectrometry (GC-MS) were used to verify the feasibility of the ATLD-SMR method. Then, PM10 samples (receptor) at five locations and TSP samples of ten pollution sources were collected during July and August, 2018 in Loudi City, China. The collected samples were measured by GC-MS. PAHs were used as tracers and their concentrations were accurately obtained by the ATLD-SMR analysis of GC-MS data of these samples after the problems of GC-MS including baseline drift, retention-time shift and unexpected peaks overlapping were successfully resolved. The highest concentrations of individual PAH in these samples were for phenanthrene and benzo [a] pyrene (40.76 ng m-3 and 39.63 ng m-3 in Liangang steel-making workshop, respectively). Last, a relative contribution matrix of the source to the receptor was estimated by the ATLD-SMR method. The proposed method was employed to apportion the source contributions to PM10 particles at five locations and reasonable results were obtained, thus presenting a promising tool for source apportionment of complex ambient particulate matter.

Automatic peak detection coupled with multivariate curve resolution–alternating least squares for peak resolution in gas chromatography–mass spectrometry

Gas chromatography–mass spectrometry (GCMS) has been extensively used in complex sample analysis for the high-throughput characterization of volatile and semivolatile compounds. However, the accurate extraction of compound information remains challenging. Here, we present a combined algorithm strategy for GCMS data analysis to accurately screen metabolites across groups. First, chromatographic peaks in a total ion chromatogram (TIC) are extracted by using a Gaussian smoothing strategy and aligned on the basis of their mass spectra by a dynamic programing algorithm. The aligned TIC peaks are then registered into a component list table by applying a nearest-neighbor clustering algorithm. Significantly expressed TIC peaks among groups are screened through statistical analysis, such as ANOVA. Second, a chemometric method of multivariate curve resolution–alternating least squares for the peak resolution of the screened TIC peaks is utilized to retrieve the chromatographic and mass spectral profiles of coeluted components. The developed strategy is employed for the analysis of standard and complex plant sample datasets. Results indicate that our methodology is comparable with several state-of-the-art methods that are widely used in GC–MS-based metabolomics.

Study on Variation of Non-polar Metabolites in Gossypium hirsutum L. under Water Stress Condition Using Gas Chromatography-mass Spectroscopy Technique

Present work was aimed at studying the variation of non polar metabolites content in Gossypium hirsutum L. under water stress condition using a gas chromatography-mass spectrometry (GC-MS) technique. A total of 17 non-polar metabolites were detected in control and water stressed G. hirsutum leaf. The major metabolites were quinoline derivative (26.37±0.29%), 2- methylhexadecan-1-ol (7.47±0.07%), phytol (7.71±0.02%), myristic acid (5.94±0.04%), hexadecanol (14.30±0.94%), nonadecane (1.67±0.05%) and palmitic acid (3.20±1.39%). Fourteen metabolites were detected in control and water stressed G. hirsutum stem. The major metabolites were dodecene (1.67±0.11%), L-lysine (0.65±0.06%), dibutylphthalate (5.06±1.88%), linoleic acid (10.26±0.07%), campesterol (0.87±0.04%) and stigmasterol (1.13±0.55%). Statistical analysis of GC-MS data was carried out by Mann-Whitney U test without normal distribution using statistical software SYSTAT version 12.0. Significant variation in the content of the most of the metabolites were observed between control and water stressed leaf or stem (Mann-Whitney U test, P =0.05). It concludes that the major metabolites played an important role during water stress and can be consider as metabolites responsible for water stress tolerance in G. hirsutum.

Gas chromatography-mass spectrometry data analysis algorithm based on sparse model and its application in resolving severely overlapped peaks

A Gas chromatography-mass spectrometry (GC-MS) data analysis algorithm is proposed. The mass spectrum at the top of the chromatographic peak is the spectrum to be solved. A certain amount of related reference spectra is retrieved from the spectral library, then, the equation of the chromatographic response value of each pure component is solved. A step by step strategy is used for the mass spectra retrieval. Firstly, an efficient indexing technology is used for rough selection, then, the "strong peak out with high probability" and the "extrusion" rules are used to exclude more unrelated mass spectra. A regression algorithm based on a sparse model is proposed to solve the equation of the chromatographic response value. Compared with the traditional algorithm, this algorithm can extract the main structure of the spectrum to be solved, and avoid over-fitting. The experimental results show that the proposed algorithm has a higher accuracy and smaller residual reference spectrum set, and the sparse model achieves satisfactory experimental results in the analysis of severely overlapped peaks. The proposed method provides an effective solution for resolving overlapped peaks, especially severely overlapped peaks, in GC-MS data.

Characterization and Identification of Recalcitrant Organic Pollutants (ROPs) in Tannery Wastewater and Its Phytotoxicity Evaluation for Environmental Safety.

Tannery wastewater (TWW) is of serious environmental concern to pollution control authorities, because it contains highly toxic, recalcitrant organic and inorganic pollutants. The nature and characteristics of recalcitrant organic pollutants (ROPs) are not fully explored to date. Hence, the purpose of this study was to characterize and identify the ROPs present in the treated TWW. Gas chromatography-mass spectrometry data analysis showed the presence of a variety of ROPs in the treated TWW. Results unfolded that benzyl chloride, butyl octyl phthalate, 2,6-dihydroxybenzoic acid 3TMS, dibutyl phthalate, benzyl alcohol, benzyl butyl phthalate, 4-chloro-3-methyl phenol, phthalic acid, 2'6'-dihydroxyacetophenone, diisobutyl phthalate, 4-biphenyltrimethylsiloxane, di-(-2ethy hexyl)phthalate, 1,2-benzenedicarboxylic acid, dibenzyl phthalate, and nonylphenol were present in the treated TWW. Due to endocrine disrupting nature and aquatic toxicity, the U.S. Environmental Protection Agency classified many of these as "priority pollutants" and restricted their use in leather industries. In addition, the physicochemical analysis of the treated TWW also showed very high BOD, COD, and TDS values along with high Cr and Pb content beyond the permissible limits for industrial discharge. Furthermore, phytotoxicity assessment unfolds the inhibitory effects of TWW on the seed germination, seedling growth parameters, and α-amylase activity in Phaseolus aureus L. This indicates that the TWW discharged even after secondary treatment into the environment has very high pollution parameters and may cause a variety of serious health threats in living beings upon exposure. Overall, the results reported in this study will be helpful for the proper treatment and management of TWW to combat the environmental threats.

Gas chromatography-mass spectrometry metabolomics of goat milk with different polymorphism at the αS1-casein genotype locus

Hyphenated gas chromatography-mass spectrometry (GC-MS) and multivariate data analysis techniques were used to uncover milk metabolite differences in different αS1-casein genotypes of goats. By a discriminant GC-MS metabolomics approach, we characterized milk polar metabolites of 28 goats. Animals were selected on the basis of their genotypes as 7 goats classified heterozygous for weak or null alleles, 5 for the genotype EE, 9 for the genotypes AE and BE, and finally 7 for the strong genotype AA. Low molecular weight polar metabolite profile was tightly related to the different goat genotypes, milk production, and protein levels. Results of multivariate statistical analysis of GC-MS data demonstrate that different heterozygous and homozygous genotypes expressed different metabolites such as citric and aconitic acid for the strong allele class with different sugars and polyols for the weak class.

Chemical Components of Four Essential Oils in Aromatherapy Recipe

This study focused on characterization of the chemical components of an aromatherapy recipe. The formulation consisted of four blended essential oils; rosemary oil, eucalyptus oil, pine oil and lime oil (volume ratio 6 : 2 : 1 : 1). The single and combination essential oils were identified by gas chromatography-mass spectrometry (GC-MS). The analysis of GC-MS data revealed that several components exist in the mixture. The five most important components of the blended essential oils were 1,8-cineole (35.6 %), α-pinene (11.1%), limonene (9.6%), camphor (8.4%), and camphene (6.6%). The main components of rosemary oil were 1,8-cineole (37.3%), α-pinene (19.3%), camphor (14.7%), camphene (8.8%), and β-pinene (5.5%); of eucalyptus oil 1,8-cineole (82.6%) followed by limonene (7.4%), o-cymene (4.3%), γ-terpinene (2.7%), and α-pinene (1.5%); of pine oil terpinolene (26.7%), α-terpineol (20.50%), 1-terpineol (10.8%), α-pinene (6.0%), and γ-terpineol (5.3%); and of lime oil limonene (62.9%), γ-terpinene (11.5%), α-terpineol (7.6%), terpinolene (6.0%), and α-terpinene (2.8%). The present study provided a theoretical basis for the potential application of blended essential oils to be used as an aromatherapy essential oil recipe. GC-MS serves as a suitable and reliable method for the quality control of the chemical markers.

Thorough analysis of Iranian spearmint essential oil: combination of chemometrics and gas chromatography-mass spectrometry

The components of Iranian spearmint essential oil were identified and quantified using gas chromatography-mass spectrometry (GC-MS) combined with chemometric tools. A multivariate curve resolution (MCR) approach was used to overcome the problems of background, baseline offset and overlapping/embedded peaks in GC-MS. The peaks were resolved using heuristic evolving latent projection (HELP) and multivariate curve resolution-alternating least squares (MCR-ALS) techniques. A new pure variable selection method named multivariate curve resolution-objective function equal to zero (MCR-FEQZ) is introduced and was applied in this work. The performance of this technique was evaluated and confirmed using MCR-ALS. The analysis of GC-MS data with the help of MCR techniques revealed that the five most abundant constituents of the Iranian spearmint essential oil are (+)-carvone (73.20%), limonen (14.63%), β-bourbonene (1.13%), cineole (1.10%) and 3-terpinolenone (0.82%). The results show that this essential oil could be a great source of carvone and limonene.

Chemometrics-Assisted GC-MS Analysis of Volatile and Semi-Volatile Constituents of Elettaria cardamomum

Multivariate curve resolutions (MCR) along with other chemometric techniques are proposed to improve the analysis of Iranian Elettaria cardamomum (E. cardamom) essential oil with gas chromatography-mass spectrometry (GC-MS). In addition, multivariate curve resolution-alternating least squares (MCR-ALS) is used to obtain pure elution and mass spectral profiles for the components present in each chromatographic segment as well as their relative concentrations. This strategy was also used to overcome the problems of baseline offset, asymmetric peaks, retention time shifts, and overlapped and embedded peaks occurring during GC-MS analysis. The analysis of GC-MS data revealed that 42 components exist in the E. cardamom essential oil. However, with the help of MCR and different chemometric methods, this number was extended to 73 components. The most important components of the Iranian E. cardamom are α-terpineol acetate (11.78 %), linalool (10.15 %), nerolidol (8.82 %), α-pinene (8.11 %), geranyl acetate (3.47 %),1,8-cineole (4.25 %), and γ-terpinene (3.88 %).

Maternal exposure to dioxin reduces hypothalamic but not pituitary metabolome in fetal rats: a possible mechanism for a fetus-specific reduction in steroidogenesis

2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) reduces the synthesis of pituitary gonadotropins in a fetal age-specific manner. The pituitary synthesis of gonadotropins is regulated by the hypothalamus and, thus, needs the differentiation and development of the hypothalamus requiring a number of factors including energy supply and neurotransmitters. To investigate the mechanism whereby TCDD reduces fetal gonadotropins, we carried out a comparative study on the metabolomes of the hypothalamus and pituitary using fetal and mature Wistar rats. Male fetuses at gestational day (GD)20 were removed from dams treated orally with TCDD (1 microg/kg) at GD15, and the metabolome profiles were analyzed by gas chromatography-mass spectrometry (GC-MS). The principal component analysis of GC-MS data revealed that TCDD caused a change in the profile of fetal metabolome more markedly in the hypothalamus than in the pituitary. In sharp contrast, TCDD did not cause any marked alteration in hypothalamic as well as pituitary metabolomes in male rats born of untreated dams and treated with TCDD at postnatal day 49. It was also demonstrated that a number of fetal hypothalamic components, including glutamine and gamma-aminobutyric acid, are reduced by TCDD. These results demonstrate a possibility that TCDD may reduce the metabolic activity of the hypothalamus in a fetus-specific fashion, resulting in the reduced synthesis of gonadotropins.

Supercritical fluid extraction coupled with chromatography-mass spectrometry: Hair analysis. Chromatography-mass spectrometry data processing using the CODA mathematical algorithm

The composition of medium-volatility organic compounds extracted from human hair using supercritical fluid extraction (SFE) was determined. The entire extract was trapped from a gas flow in a sorption device at the extractor outlet and thermally desorbed into a gas chromatography-mass spectrometry (GC-MS) instrument. The extracted compounds were identified either by the direct processing of GC-MS data or using the CODA mathematical algorithm. The combination of solventless SFE with the transfer of the entire extract into a GC-MS instrument with the direct analysis of GC-MS data allowed us to determine organic compounds at a level of 3.9 pg/mg hair. The CODA mathematical algorithm for the processing of GC-MS data allowed us to determine compounds at a level of 1.7 pg/mg hair; in the majority of publications, this determination limit was achieved either by selective ion detection or using a GC-MS-MS technique.