Gas Chromatography-mass Spectrometry Data Research Articles

Study protocol: the ‘Endoscope CRC’ cohort, a prospective biobank study on the development and evaluation of diagnostic and prognostic biomarker profiles for colorectal cancer and premalignant lesions

IntroductionEarly detection of colorectal cancer (CRC) and clinically relevant (advanced) adenomas leads to a significant reduction of CRC-related mortality and morbidity. However, the faecal immunochemical test (FIT) suffers from a...

Evaluation of urinary volatile organic compounds as a novel metabolomic biomarker to assess chronic kidney disease progression

BackgroundThere is a need to develop accurate and reliable non-invasive methods to evaluate chronic kidney disease (CKD) status and assess disease progression. Given it is recognized that dysregulation in metabolic pathways occur from early CKD, there is a basis in utilizing metabolomic biomarkers to monitor CKD progression. Volatile Organic Compounds (VOCs), a form of metabolomic biomarker, are gaseous products of metabolic processes in organisms which are typically released with greater abundance in disease conditions when there is dysregulation in metabolism. How urinary VOCs reflect the abnormal metabolic profile of patients with CKD status is unknown. Our study aimed to explore this.MethodsIndividuals aged 18–75 years undergoing kidney biopsy were included. Pre-biopsy urine samples were collected. All biopsy samples had an interstitial fibrosis and tubular atrophy (IFTA) grade scored by standardized assessment. Urine supernatant was extracted from residue and sampled for stir bar sorptive extraction followed by Gas chromatography–mass spectrometry (GC-MS) analysis. Post-processing of GC-MS data separated complex mixtures of VOCs based on their volatility and polarity. Mass-to-charge ratios and fragment patterns were measured for individual VOCs identification and quantification. Linear discriminant analysis (LDA) was performed to assess the ability of urinary VOCs in discriminating between IFTA 0 (‘no or minimal IFTA’ i.e. <10%, IFTA), IFTA 1 (‘mild IFTA’ i.e. 10–25% IFTA) and IFTA ≥ 2 (‘moderate or severe IFTA’ i.e. >25% IFTA). Linear regression analysis adjusting for age, sex, estimated glomerular filtration rate, diabetes mellitus (DM) status, and albuminuria was conducted to determine significantly regulated urinary VOCs amongst the groups.Results64 study participants (22 individuals IFTA 0, 15 individuals IFTA 1, 27 individuals IFTA ≥ 2) were included. There were 34 VOCs identified from GC-MS which were statistically associated with correct classification between the IFTA groups, and LDA demonstrated individuals with IFTA 0, IFTA 1 and IFTA ≥ 2 could be significantly separated by their urinary VOCs profile (p < 0.001). Multivariate linear regression analysis reported 4 VOCs significantly upregulated in the IFTA 1 compared to the IFTA 0 group, and 2 VOCs significantly upregulated in the IFTA ≥ 2 compared to the IFTA 1 group (p < 0.05). Significantly upregulated urinary VOCs belonged to one of four functional groups - aldehydes, ketones, hydrocarbons, or alcohols.ConclusionsWe report novel links between urinary VOCs and tubulointerstitial histopathology. Our findings suggest the application of urinary VOCs as a metabolomic biomarker may have a useful clinical role to non-invasively assess CKD status during disease progression.

Grade Identification of Strong-flavor Raw Liquor Based on GC-MS Combined with Spearman-KPCA Feature Extraction

Taking different grades of strong-flavor raw liquor as the research object, gas chromatography-mass spectrometry (GC-MS) technology was used to obtain the volatile components mapping data of raw liquor, and Spearman correlation coefficient (Spearman) combined with principal component analysis (PCA) and kernel principal component analysis (KPCA) was used to realize the secondary feature extraction of the GC-MS data, and then combined with the support vector machine (SVM), extreme gradient boosting (XGBoost), and BP neural network to establish the raw liquor grades identification model, respectively. The results show that the prediction accuracy of the grade identification model based on Spearman-KPCA dimensionality reduction data is better, in which the Spearman-KPCA-BP neural network model has the best classification effect, and the accuracy of the correction set and prediction set reaches 99.44% and 96.10%, respectively. Research shows that the principal components extracted based on Spearman-KPCA secondary features can better characterize the characteristic information of different grades of raw liquor. Combined with the BP neural network model, it can effectively realize the identification of different grades of raw liquor. It is an effective method for identifying the grade of raw liquor.

Artificial intelligence tools and concepts give access to authenticity and quality information in Brazilian olive oil volatilome

Brazil is the world's second largest importer of olive oil and stared to produce its own oil quite recently in two distinct geographic areas. The production is still very small, and studies regarding oil composition are scarce. Determination of discriminant differences are useful to support appeals regarding origin indication and to detect fraud and adulteration with oils from other countries. In this work the Brazilian extra virgin olive oils (EVOOs) volatilome is explored by Artificial Intelligence (AI) tools developed on multiple headspace solid-phase microextraction (MHS-SPME) combined with comprehensive 2D gas chromatography-mass spectrometry and flame ionization detection (GC×GC-MS/FID) data. Using MHS-SPME, external standard calibration, and FID predicted relative response factors (RRF), the accurate quantification of 51 informative volatile compounds was carried out and the odorants responsible for key-positive sensory attributes were used to generate distinctive aroma blueprints aligned with the AI smelling based on sensomics. As authenticity and origin assessment decision-making tool, augmented visualization by computer vision was applied on volatilome 2D fingerprints. By this approach, extra-virgin olive oil samples (n=35), from two olive cultivars (Arbequina and Koroneiki) harvested in the main producing regions in Brazil (Rio Grande do Sul and Serra da Mantiqueira) in 2021 and 2022, were effectively discriminated and mislabeled products regarding their geographical origin were, for the first time, promptly identified.

Chemotaxonomy of Southeast Asian Peperomia (Piperaceae) Using High-Performance Thin-Layer Chromatography Colour Scale Fingerprint Imaging and Gas Chromatography–Mass Spectrometry

The morphological characters of Southeast Asia’s indigenous Peperomia species are very similar, especially in their flower structures. The flowers are simple, hermaphrodite and lack a perianth. Therefore, many species are hard to distinguish using morphological characters alone. Here, we apply chemometric data for species identification and classification, gathered using multiwavelength detection combined with the colour scale High-Performance Thin-Layer Chromatography (HPTLC) fingerprinting procedure and chemical compounds determined by Gas Chromatography–Mass Spectrometry (GC-MS). Fourteen taxa were investigated using hexane, ethyl acetate and ethanol solvent extractions. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used with the colour scale fingerprints to classify the Peperomia species. The PCA and HCA using the chromatogram profile from hexane divided the taxa into six groups compared to the profile from ethyl acetate and ethanol, which each detected seven groups. The chromatogram from the combined dataset of all three solvents can differentiate all the species. The GC-MS data detected a total of 40 compounds from the hexane extract, and these differed among Peperomia species. This approach based on HPTLC fingerprinting and GC-MS analysis can therefore be used as a tool for authentication and identification studies of Peperomia species.

Probabilistic PARAFAC2.

The Parallel Factor Analysis 2 (PARAFAC2) is a multimodal factor analysis model suitable for analyzing multi-way data when one of the modes has incomparable observation units, for example, because of differences in signal sampling or batch sizes. A fully probabilistic treatment of the PARAFAC2 is desirable to improve robustness to noise and provide a principled approach for determining the number of factors, but challenging because direct model fitting requires that factor loadings be decomposed into a shared matrix specifying how the components are consistently co-expressed across samples and sample-specific orthogonality-constrained component profiles. We develop two probabilistic formulations of the PARAFAC2 model along with variational Bayesian procedures for inference: In the first approach, the mean values of the factor loadings are orthogonal leading to closed form variational updates, and in the second, the factor loadings themselves are orthogonal using a matrix Von Mises-Fisher distribution. We contrast our probabilistic formulations to the conventional direct fitting algorithm based on maximum likelihood on synthetic data and real fluorescence spectroscopy and gas chromatography-mass spectrometry data showing that the probabilistic formulations are more robust to noise and model order misspecification. The probabilistic PARAFAC2, thus, forms a promising framework for modeling multi-way data accounting for uncertainty.

Identification of volatile compounds and evaluation of certain phytochemical properties of Turkish propolis.

The purpose of this study was to assess volatile component profiles and the antioxidant activity of propolis samples from eight different locations in Türkiye. α-Pinene, β-pinene, 3-carene, limonene, 2-acetylfuran, benzaldehyde, acetic acid, benzoic acid, longifolene, isopentyl acetate, m-cymene, styrene, and δ-cadinene were the most common volatile components found in the most of propolis samples. Principal component analysis (PCA) and hierarchical cluster analysis were performed on the gas chromatography-mass spectrometry (GC-MS) data to identify trends and clusters in the propolis samples. As a result of the PCA, the common components in all propolis were m-cymene, decanal, α-pinene, limonene, pinocarvone, benzaldehyde, and butanoic acid. The samples had 2,2-diphenyl-1-picrylhydrazyl radical (DPPH) inhibition activity ranging from 19.2% to 92.5% and 2,2'-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) values ranging from 480 to 1370 μM trolox/g extract. The total phenolic content (TPC) ranged from 10,900 to 34,033 mg GAE/100 g. Compared with butylated hydroxytoluene (BHT) (control), all but one of the propolis samples exhibited higher DPPH activity. In addition, ABTS levels of propolis extracts were higher than those of BHT. These results unequivocally show that Turkish propolis has remarkable antioxidant qualities, which makes it a viable option for addition to food and medicine products.

Characterization of the Phytotoxic Potential of Seven Copaifera spp. Essential Oils: Analyzing Active Compounds through Gas Chromatography-Mass Spectrometry Molecular Networking.

In recent years, there has been a need for environmentally friendly compounds for weed management in agriculture. This study is aimed to assess the phytotoxic constituents of oils obtained from oleoresins of seven Copaifera species (known as copaiba oils). Copaiba oils were separated from the resins by hydro-distillation, and the distillates were analyzed using gas chromatography-mass spectrometry (GC-MS) to characterize their chemical compositions. Multivariate analyses and molecular networking of GC-MS data were conducted to discern patterns in the chemical composition and phytotoxic activity of the oils, with the aim of identifying key compounds associated with phytotoxic activity. Seed germination bioassay revealed strong or complete germination inhibition against the monocot, Agrostis stolonifera but not the dicot Lactuca sativa. GC-MS analysis showed variations in composition among Copaifera species with some common compounds identified across multiple species. Caryophyllene oxide and junenol were associated with the observed phytotoxic effects. Automated flash chromatography was used to isolate the major compounds of the oils. Isolated compounds exhibited differing levels of phytotoxicity compared to the oils, suggesting the importance of interactions or synergism among oil components. These findings highlight the potential of copaiba oils as natural herbicidal agents and underscore the importance of considering species-specific responses in weed management strategies.

Characterization of the Cytotoxic Compounds of Lactarius salmonicolor R. Heim and Leclair by Gas Chromatography-Mass Spectrometry and Chemometrics

The cytotoxic activities of apolar extract of Lactarius salmonicolor and its fractions and subfractions were screened against colon (CaCo-2), prostate (LNCaP), lung (H1299), and breast (MCF-7) cancerous cell lines. The main extract of Lactarius salmonicolor that was cytotoxic to CaCo-2 (EC50: 137.1 ± 2.7 µg/mL) and LNCap (EC50: 131.2 ± 2.6 µg/mL) mainly contains fatty acids (99.9%). To characterize the cytotoxic activity, it was fractionated by a silica gel column. Two cytotoxic fractions were re-fractionated to obtain subfractions. The extract, four cytotoxic fractions, and all subfractions were analyzed by gas chromatography-mass spectrometry (GC-MS). The GC-MS data and the cytotoxic activity (EC50) results were classified using principal component analyses (PCA). According to PCA, the cytotoxic fractions containing abundant benzoic acid (0.01%–0.35%), cinnamic acid (0.01%–0.36%), azelaic acid (0.01%–0.92%), 2,4-decadienal (0.01%–0.12%), (Z)-9-palmitoleic acid (0.01%–1.09%), 18-hydroxy 2,4-diene oleic acid (0.01%–26.71%), arachidic acid (0.01%–6.70%), desmosterol (0.01%–12.27%), and steroids (0.01%–9.51%) were clustered. LSP.9-10, which causes another cluster, is caused by 8,11,14- eicosatrienoic acid, 11,14,17-eicosatrienoic acid, 9(Z)-decenoic acid compounds, and saturated fatty acids with 10 and 16 carbons. Hence, these compounds may be responsible for the cytotoxic activity. This study demonstrates that GC-MS and chemometric analysis can identify the components responsible for biological activity in combination with biological activity data.

Characterization of the flavor profile of UHT milk during shelf-life via volatile metabolomics fingerprinting combined with chemometrics

Ultra-high temperature (UHT) milk is popular among consumers. However, its flavor and texture change in its shelf life. Flavor is highly determinative for the success of dairy products and for consumers’ willingness to buy. It is important to milk producers to ensure the optimal flavor of their products in the shelf life. In order to be able to control and predict the flavor quality of UHT milk during the shelf life, this study compared the variations in sensory quality, volatile aroma release and backbone flavor factors and developed a discriminant model to assess flavor quality based on flavouromics data of five competing milk sample during storage. Using partial least squares discriminant analysis (PLS-DA) with Electronic-nose (E-nose) data excellent classification sensitivity and specificity were achieved compared to models based on gas chromatography-mass spectrometry (GC–MS) data. The PLS-DA model using E-nose data exhibited a 100% correct classification rate for the storage period, and a 92% correct rate based on the eight variable importance in the projection (VIP) elements screened for volatile components from different groups. The discriminative model developed herein based on E-nose combined with chemometrics demonstrated advantages such as speed, efficiency, and environmental friendliness. This method shows promise as a precise tool for analyzing aroma changes in UHT milk during its shelf life, and provide support for controlling the flavor substances and milk product development.

Antiparasitic activity of the steroid-rich extract of Schima wallichii against poultry cestode.

Schima wallichii Korth., commonly known as the needlewood tree (family Theaceae) has therapeutic uses in traditional Mizo medicine for human helminthiasis and serves as a balm against ectoparasites in animals. Although the medicinal properties have been studied experimentally, its use as a traditional anthelmintic remains unexplored. This study aimed to analyze the chemical components and antiparasitic activity of S. wallichii. The chemical analysis of S. wallichi bark extracts was conducted focusing on the secondary metabolites using petroleum ether, chloroform, and methanol. Gas chromatography-mass spectrometry (GC-MS) was used to identify the specific compounds. An anthelmintic susceptibility test was carried out against Raillietina tetragona, intestinal cestode parasite of fowl. The methanol extract yielded the highest concentrations of alkaloids, carbohydrates, glycosides, sterols, saponins, and tannins among all the extracts. Sterols were the most abundant compounds in all extracts, with flavonoids being absent. Secondary metabolites were largely absent in the petroleum ether and chloroform extracts. The GC-MS data identified cholest-22-ene-21-ol as the major steroid component. The cestode parasite was inhibited in a concentration-dependent manner by the plant extract. The plant extract's anthelmintic activity was evident through observable damage to the parasite's outer structure. Phytosterols in S. wallichii bark are responsible for its anthelmintic properties. The mechanism and pharmaceutical properties of the anthelmintic molecule require further exploration.

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 &lt;i&gt;Sodhita Semecarpus anacardium&lt;/i&gt; L. Drupe in Wistar Albino Rats and its Prediction Using &lt;i&gt;In-Silico&lt;/i&gt; 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.