Due to differences in raw materials and production processes, different spirits exhibit various flavor even if undergo distillation operation. In this study, sensory analysis could clearly distinguish 5 types spirits, and had been validated through quantitative targeted flavoromics analysis. Consequently, 44 potential differential markers between 5 types spirits were screened. Among, 34 definite differential markers were further confirmed to be highly correlated with target sensory attributes and could effectively distinguish types of spirits. Ultimately, 14 key differential markers (including 2-methylbutane, linalool, acetaldehyde, d-limonene, β-myrcene, phenylethyl alcohol, phenethyl acetate, heptyl formate, ethyl octanoate, ethyl decanoate, ethyl pentanoate, ethyl hexanoate, hexanoic acid, and ethyl hexadecanoate) could reveal the chemical sources of spirit sensory and serve as targets for identifying different types of spirits. Overall, the results of flavoromic characterization of 5 types spirits provided a significant step forwards in understanding of differentiation of spirits by sensory coupled with quantitative, and statistical analysis.

BACKGROUND: Dalitong Granules (DLT), a potent traditional Chinese medicine known for its ability to promote gastrointestinal motility, is widely used in clinical practice for the treatment of Functional Dyspepsia (FD). Despite the remarkable clinical efficacy of DLT, the specific components responsible for its effectiveness remains unclear.
OBJECTIVE: The study aimed to identify potential active ingredients of DLT for treating FD through spectrum-effect relationship analysis, multivariate statistical analysis and network pharmacology analysis. The efficacy of these identified compounds was subsequently validated using the zebrafish intestinal peristalsis model.
METHODS: The fingerprints of various solvent-extracted DLT were analyzed using high performance liquid chromatography coupled with tandem high-resolution mass spectrometry. The intestinal motility-promoting activities of DLT extracted by different solvents were evaluated through an intestinal propulsion test in mice. Potential therapeutic substances in DLT for treating FD were screened via chemometric analysis based on spectrum-effect relationship analysis. The correlation between the intensity of common peaks in the total ion chromatogram and the pharmacodynamic indices was assessed using multivariate statistical analysis. Additionally, given the complexity of traditional Chinese medicine, which comprises multiple components and targets, a network pharmacology analysis was performed to investigate the potential active ingredients in DLT. Finally, the pharmacological effects of these compounds in DLT were validated using a zebrafish intestinal motility model.
RESULTS: Through spectral-effect relationships analysis and network pharmacology analysis, it was determined that ten ingredients in DLT contribute to the promotion of intestinal motility. In a zebrafish intestinal motility model, it was observed that eight chemicals (excluding tetrahydropalmatine) demonstrate favorable activity of promoting gastrointestinal motility. These findings suggest that these ingredients may serve as potential therapeutic agents for improving gastric motility disorders.
CONCLUSIONS: This study employed spectral-effect relationship and network pharmacology analysis to identify the active ingredients in DLT. The findings were then validated using a zebrafish intestinal peristalsis model. These results provide a scientific foundation for the clinical application of DLT as a key traditional herbal formula for managing FD.

Cannabis sativa, a globally commercialized plant used for medicinal, food, fiber production, and recreation, necessitates effective identification to distinguish legal and illegal varieties in forensic contexts. This research utilizes multivariate statistical models and Machine Learning approaches to establish correlations between specific genotypes and tetrahydrocannabinol (Δ9-THC) content (%) in C. sativa samples. 132 cannabis leaves samples were obtained from legal growers in Piedmont, Italy, and illegal drug seizures in Turin. Samples were genetically profiled using a 13-loci STR multiplex and their Δ9-THC content was detected through quantitative GC-MS analysis. This study aims to assess the use of supervised classification modelling on genetic data to distinguish cannabis samples into legal and illegal categories, revealing distinct clusters characterized by unique allele profiles and THC content. t-distributed Stochastic Neighbor Embedding (t-SNE), Random Forest (RF) and Partial Least Squares Regression (PLS-R) were executed for the machine learning modelling. All the tested models resulted effective discriminating between legal samples and illegal. Although further validation is necessary, this study presents a novel forensic investigative approach, potentially aiding law enforcement in significant marijuana seizures or tracking illicit drug trafficking routes.

Chuju, a cultivar of Chrysanthemum morifolium, has been traditionally cultivated for over 2000 years in China for both ornamental and medicinal purposes. To date, investigations into the chemical composition of this plant have indicated that it contains compounds with extensive biological activities, although detailed information on the chemical composition of Chuju remains scarce. In the present study, the chemical compositions of Chuju flowers were investigated across five sites in the core Chuju planting area in Anhui province, China. Analytical pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS) was used to explore variations in flower chemical fingerprints from different Chuju planting sites. The study identified approximately 200 components in Chuju flowers and stems, including high levels of fatty acids, lipids, polysaccharides and terpenoids. Multivariate statistical analysis indicated that 16 chemical compounds were influential determinants of the chemical fingerprint and could be used to distinguish two clusters in the five core planting areas. The established Py-GC/MS analytical workflow could provide a basis for determining the chemical fingerprints of Chuju and help elucidate that products contain a reproducible content of bioactive compounds and overall quality for potential development of health and medicinal purposes.

Traditional Chinese medicine (TCM) and its preparations have become increasingly popular in recent years. Nonetheless, due to the high complexity of the compounds in Traditional Chinese Patent Medicine (TCPM), the quality differences between different dosage forms and products from various manufacturers pose numerous challenges and difficulties in quality evaluation. The Qiangli Tianma Duzhong (QLTMDZ) prescription, comprising twelve TCM, is widely used in China. Despite its prevalence, current research on QLTMDZ is limited and lacks in-depth and systematic analysis of the chemical composition of the prescription. In this study, a comprehensive strategy was proposed for characterizing the chemical profile of QLTMDZ based on UHPLC-Q-TOF-MS. A total of 122 compounds were identified in QLTMDZ under both positive and negative ion modes. Subsequently, multivariate statistical methods such as principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) were conducted in the MS-DIAL software to further elucidate quality differences among 55 batches of QLTMDZ samples from seven manufacturers. Lastly, multiple reaction monitoring (MRM) mode was utilized in conjunction with UHPLC-QQQ-MS, for the precise quantification of the identified 24 compounds within the QLTMDZ preparation and providing supplementary information in quality evaluation. The established analytical method in this study is sensitive and efficient, enabling qualitative and quantitative analysis of the chemical constituents within QLTMDZ. The application of multivariate statistical analyses effectively discriminates samples based on different dosage forms and manufacturers, thereby providing new research directions and scientific support for further studies on the quality control of the prescription.

The authenticity of fish products has become a widespread issue in markets due to substitution and false labeling. Lipidomics combined with chemometrics enables the fraudulence identification of food through the analysis of a large amount of data. This study utilized ultra-high-performance liquid chromatography (UHPLC)-QE Orbitrap MS technology to comprehensively analyze the lipidomics of commercially available basa catfish and sole fish. In positive and negative ion modes, a total of 779 lipid molecules from 21 lipid subclasses were detected, with phospholipid molecules being the most abundant, followed by glycerides molecules. Significant differences in the lipidome fingerprinting between the two fish species were observed. A total of 165 lipid molecules were screened out as discriminative features to distinguish between basa catfish and sole fish, such as TAG(16:0/16:0/18:1), PC(14:0/22:3), and TAG(16:1/18:1/18:1), etc. This study could provide valuable insights into authenticating aquatic products through comprehensive lipidomics analysis, contributing to quality control and consumer protection in the food industry.

This study aimed to explore the pharmacodynamics and mechanisms of different processing methods of Ligustrum lucidum Ait. (LLA) in addressing kidney-yin deficiency (KYD). Forty-eight Sprague-Dawley rats were divided into eight groups based on their weight. The KYD model was established by intragastric administration of levothyroxine sodium. Each group was administered the corresponding treatment for 15 consecutive days. The general condition of the rats during the treatment period was observed. In addition, the levels of cyclic adenosine monophosphate (cAMP), cyclic guanosine monophosphate (cGMP), and the ratio of cAMP to cGMP in the serum of rats from different groups were measured. Serum samples were analyzed using the ultra-performance liquid chromatography (UPLC)-Orbitrap Fusion MS technique for metabolomics analysis. Compared with the model group, the general condition of the rats in the wine-steamed L. lucidum group (WL) and salt-steamed L. lucidum group (SSL) groups showed significant improvement. The serum levels of cAMP, cGMP, and the cAMP-to-cGMP ratio tended to return to normal. Metabolic analysis identified 38 relevant biomarkers and revealed 3 major metabolic pathways: phenylalanine, tyrosine, and tryptophan biosynthesis; phenylalanine metabolism; and sphingolipid metabolism. The different processing methods of LLA demonstrated therapeutic effects on KYD in rats, likely related to the restoration of disturbed metabolism by adjusting the levels of endogenous metabolites in the kidney. The SSL demonstrated significantly superior effects compared with the other four types of LLA processed products.

Due to rapid urbanization and industrial growth, groundwater globally is continuously deteriorating, posing significant health risks to humans. This study employed a comprehensive methodology to analyze groundwater in the Western Banat Plain (Serbia). Using Piper and Gibbs plots, hydrogeochemistry was assessed, while the entropy-weighted water quality index (EWQI) was used to evaluate groundwater quality. Pollution sources were identified using positive matrix factorization (PMF) accompanied by Pearson correlation and hierarchical cluster analysis, while Monte Carlo simulation assessed health risks associated with groundwater consumption. Results showed that groundwater, mainly Ca-Mg-HCO3 type, is mostly suitable for drinking. Geogenic pollution, agricultural activities, and sewage were major pollution sources. Consumption of contaminated groundwater poses serious non-carcinogenic and carcinogenic health risks. Additionally, arsenic from geogenic source was found to be the main health risks contributor, considering its worryingly elevated concentration, ranging up to 364 μg/L. These findings will be valuable for decision-makers and researchers in managing groundwater vulnerability. PRACTITIONER POINTS: Groundwater is severely contaminated with As in the northern part of the study area. The predominant hydrochemical type of groundwater in the area is Ca-Mg-HCO3. The PMF method apportioned three groundwater pollution sources. Monte Carlo identified rock dissolution as the primary health risk contributor. Health risks and mortality in the study area are positively correlated.

Mechanization has emerged as a focal point in the modernization of traditional enterprises, offering standardized production and labor reduction benefits. However, little is known about how mechanization affects the microbiota and metabolite profiles of Daqu. To address this gap, we conducted a comprehensive comparison between traditional and mechanical sauce-flavor Daqu using a multi-omics approach. Results showed that mechanical Daqu exhibited higher acidity, amino acid nitrogen and enzyme activity, alongside lower fat and moisture levels. Following mechanization, lactic acid bacteria (LAB), Staphylococcus, Aspergillus and Saccharomycopsis were enriched and identified as biomarkers, whereas Oceanobacillus, Monascus and Scopulariopsis were notably decreased. Furthermore, significant disparities in metabolic profiles were observed between the two types of Daqu based on GC-MS, GC-IMS, and LC-MS/MS analyses. The content of volatile compounds was significantly higher in mechanical Daqu (332.82 ± 22.69 mg/kg), while that of non-volatile compounds was higher in traditional Daqu (753.44 ± 41.82 mg/kg). Moreover, OPLS-DA models identified 44 volatile and 31 non-volatile compounds as differential metabolites. Multivariate statistical analysis indicated that bacteria and fungi primarily contributed to protease and saccharification activities, respectively. Additionally, the co-occurrence network revealed that Oceanobacillus and Scopulariopsis were closely associated with non-volatile compound formation, while LAB and Rhizopus significantly influenced volatile compound production. These findings elucidate the multi-dimensional relationship between mechanization and Daqu quality, offering insights to advance the modernization of traditional industries.

In this study, the distinctive chemical fingerprints that contribute to the flavor characteristics of various protein materials, such as insects, plant-based protein, and livestock, were investigated. In edible-insects (Tenebrio molitor and Protaetia brevitarsis), aldehydes and cyclic volatile compounds were the predominant volatile components and had distinct flavor characteristics such as cheesy, sharp, green, floral, and sweet. In contrast, the relatively high levels of pyrazines and furans in plant-based protein materials, such as textured vegetable and pea protein. They included unique flavor properties characterized by sweet, fatty, grassy, creamy, and roasted. The primary volatile chemical group detected in livestock protein materials, such as a pork and a beef, was ketones. The pork sample showed specific flavors, such as alcoholic, green, and fruity, while a beef presented distinctive flavor, including creamy, fruity, and alcoholic. Based on the results, this research provided the understanding of the flavor aspects of diverse protein materials.