Project description:Curcuma, a genus of rhizomatous herbaceous species, has been used as a spice, traditional medicine, and natural dye. In this study, the metabolite profile of Curcuma extracts was determined using gas chromatography-time of flight mass spectrometry (GC/TOF MS) and ultrahigh-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC/Q-TOF MS) to characterize differences between Curcuma aromatica and Curcuma longa grown on the Jeju-do or Jin-do islands, South Korea. Previous studies have performed primary metabolite profiling of Curcuma species grown in different regions using NMR-based metabolomics. This study focused on profiling of secondary metabolites from the hexane extract of Curcuma species. Principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) plots showed significant differences between the C. aromatica and C. longa metabolite profiles, whereas geographical location had little effect. A t-test was performed to identify statistically significant metabolites, such as terpenoids. Additionally, targeted profiling using UPLC/Q-TOF MS showed that the concentration of curcuminoids differed depending on the plant origin. Based on these results, a combination of GC- and LC-MS allowed us to analyze curcuminoids and terpenoids, the typical bioactive compounds of Curcuma, which can be used to discriminate Curcuma samples according to species or geographical origin.
Project description:The volatiles in coffee play an important part in the overall flavor profile. In this study, GC-TOF/MS and GC×GC-TOF/MS were used to detect the volatile organic compounds (VOCs) in coffee samples of three different brands at three states (bean, powder, and brew). The differences between the two methods in characterizing VOCs were analyzed using the Venn diagram and PCA (principal component analysis). The important aroma-contributing compounds were further compared and analyzed. The results of the venn diagrams of different coffee samples showed that most VOCs existed in 2-3 kinds of coffee. The PCA of VOCs in different coffee samples showed that the VOCs detected by GC-TOF/MS could distinguish the coffee samples in the different states. GC×GC-TOF/MS was suitable for the further identification and differentiation of the different brands of coffee samples. In addition, pyridine, pyrrole, alcohols, and phenols greatly contributed to distinguishing coffee in three states, and alcohols greatly contributed to distinguishing the three brands of coffee.
Project description:Field olfactometry is one of the sensory techniques used to determine odour concentration, in atmospheric air, directly in emission sources. A two-dimensional gas chromatography with time of flight mass spectrometer (GC×GC-TOF-MS) allows performing the chemical characterization of various groups of chemical compounds, even in complex mixtures. Application of these techniques enabled determination of odour concentration level in atmospheric air in a vicinity of the oil refinery and the neighbouring wastewater treatment plant. The atmospheric air samples were analysed during a time period extending from February to June 2016. Based on the GC×GC-TOF-MS analysis and odour threshold values, the theoretical odour concentrations were calculated and compared with the odour concentrations determined by field olfactometry technique. The investigations revealed that higher values of odour concentration were obtained with the field olfactometry technique where odour analysis was based on holistic measurement. It was observed that the measurement site or meteorological conditions had significant influence on odour concentration level. The paper also discusses the fundamental analytical instruments utilized in the analysis of odorous compounds and their mixtures.
Project description:Humans interact with thousands of chemicals. This study aims to identify substances of emerging concern and in need of human health risk evaluations. Sixteen pooled human serum samples were constructed from 25 individual samples each from the National Institute of Environmental Health Sciences' Clinical Research Unit. Samples were analyzed using gas chromatography (GC) × GC/time-of-flight (TOF)-mass spectrometry (MS) in a suspect screening analysis, with follow-up confirmation analysis of 19 substances. A standard reference material blood sample was also analyzed through the confirmation process for comparison. The pools were stratified by sex (female and male) and by age (≤45 and >45). Publicly available information on potential exposure sources was aggregated to annotate presence in serum as either endogenous, food/nutrient, drug, commerce, or contaminant. Of the 544 unique substances tentatively identified by spectral matching, 472 were identified in females, while only 271 were identified in males. Surprisingly, 273 of the identified substances were found only in females. It is known that behavior and near-field environments can drive exposures, and this work demonstrates the existence of exposure sources uniquely relevant to females.
Project description:Maillard reaction products (MRPs) with roasted/broth flavors were prepared and analyzed for the resulting flavor differences. The identification of volatile compounds in MRPs was carried out by GC-MS and GC × GC-ToF-MS. A total of 88 compounds were identified by GC-MS; 130 compounds were identified by GC × GC-ToF-MS, especially acids and ketones were identified. Principal component analysis (PCA) was used to visualize the volatile compounds, and the roasted/broth flavors were differentiated. The contents and types of pyrazines were more in roasted flavors; thiol sulfides and thiophenes were more in broth flavors. All in all, the differences in volatile compounds producing roasted/broth flavors were studied through the cysteine-xylose-glutamate Maillard reaction system, which provided a theoretical basis for the future use of Maillard reaction to simulate meat flavor.
Project description:ObjectiveEphedra, widely used in clinical practice as a medicinal herb, belongs to the genus Ephedra in the family Ephedraceae. However, the presence of numerous Ephedra varieties and variants requires differentiation for accurate identification.MethodsIn this study, we employed headspace gas chromatography mass spectrometry (HS-GC-MS), ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS), and global natural products social molecular networking (GNPS) for chemical component identification. Chemometric analysis was used to analyze the differential components. Metabolic analysis and Kyoto encyclopedia of genes and genomes (KEGG) enrichment were utilized to explore the synthesis pathways of different components.ResultA total of 83 volatile and 79 non-volatile components were identified in Ephedra species. Differential analysis revealed that among the eight Ephedra stems, 18 volatile and 19 non-volatile differential compounds were discovered, whereas Ephedra roots exhibited 21 volatile and 17 non-volatile markers. Volatile compounds were enriched in four synthetic pathways, while non-volatile components were enriched in five pathways among the differentiated components.ConclusionThis study is the first to conduct a comparative analysis of chemical components in different Ephedra species and parts. It provides a foundational reference for authenticating Ephedra herbs, evaluating medicinal resources, and comparing quality in future studies.
Project description:Reliable methods are always greatly desired for the practice of food inspection. Currently, most food inspection techniques are mainly dependent on the identification of special components, which neglect the combination effects of different components and often lead to biased results. By using Chinese liquors as an example, we developed a new food identification method based on the combination of machine learning with GC × GC/TOF-MS. The sample preparation methods SPME and LLE were compared and optimized for producing repeatable and high-quality data. Then, two machine learning algorithms were tried, and the support vector machine (SVM) algorithm was finally chosen for its better performance. It is shown that the method performs well in identifying both the geographical origins and flavor types of Chinese liquors, with high accuracies of 91.86% and 97.67%, respectively. It is also reasonable to propose that combining machine learning with advanced chromatography could be used for other foods with complex components.
Project description:Militarine, a natural glucosyloxybenzyl 2-isobutylmalate, isolated from Bletilla striata, was reported with a prominent neuroprotective effect recently. The limited information on the metabolism of militarine impedes comprehension of its biological actions and pharmacology. This study aimed to investigate the metabolite profile and the distribution of militarine in vivo, which help to clarify the action mechanism further. A total of 71 metabolites (57 new metabolites) in rats were identified with a systematic method by ultra-high-performance liquid chromatography combined with quadrupole time-of-flight tandem mass spectrometry (UPLC-Q-TOF-MS/MS). The proposed metabolic pathways of militarine include hydrolyzation, oxidation, glycosylation, esterification, sulfation, glucuronidation and glycine conjugation. Militarine and its metabolites were distributed extensively in the treated rats. Notably, six metabolites of militarine were identified in cerebrospinal fluid (CSF), which were highly consistent with the metabolites after oral administration of gastrodin in rats. Among the metabolites in CSF, five of them were not reported before. It is the first systematic metabolic study of militarine in vivo, which is very helpful for better comprehension of the functions and the central nervous system (CNS) bioactivities of militarine. The findings will also provide an essential reference for the metabolism of other glucosylated benzyl esters of succinic, malic, tartaric and citric acids.
Project description:Complex mixtures of polycyclic aromatic hydrocarbons (PAHs) are difficult to resolve because of the high degree of overlap in compound vapor pressures, boiling points, and mass spectral fragmentation patterns. The objective of this research was to improve the separation of complex PAH mixtures (including 97 different parent, alkyl-, nitro-, oxy-, thio-, chloro-, bromo-, and high molecular weight PAHs) using GC × GC/ToF-MS by maximizing the orthogonality of different GC column combinations and improving the separation of PAHs from the sample matrix interferences, including unresolved complex mixtures (UCM). Four different combinations of nonpolar, polar, liquid crystal, and nanostationary phase columns were tested. Each column combination was optimized and evaluated for orthogonality using a method based on conditional entropy that considers the quantitative peak distribution in the entire 2D space. Finally, an atmospheric particulate matter with diameter <2.5 ?m (PM(2.5)) sample from Beijing, China, a soil sample from St. Maries Creosote Superfund Site, and a sediment sample from the Portland Harbor Superfund Site were analyzed for complex mixtures of PAHs. The highest chromatographic resolution, lowest synentropy, highest orthogonality, and lowest interference from UCM were achieved using a 10 m × 0.15 mm × 0.10 ?m LC-50 liquid crystal column in the first dimension and a 1.2 m × 0.10 mm × 0.10 ?m NSP-35 nanostationary phase column in the second dimension. In addition, the use of this column combination in GC × GC/ToF-MS resulted in significantly shorter analysis times (176 min) for complex PAH mixtures compared to 1D GC/MS (257 min), as well as potentially reduced sample preparation time.