With the increased incidence of wine fraud, a fast and reliable method for wine certification has become a necessary prerequisite for the vigorous development of the global wine industry. In this study, a classification strategy based on three-dimensional fluorescence spectroscopy combined with chemometrics was proposed for oak-barrel and stainless steel tanks with oak chips aged wines. Principal component analysis (PCA), partial least squares analysis (PLS-DA), and Fisher discriminant analysis (FDA) were used to distinguish and evaluate the data matrix of the three-dimensional fluorescence spectra of wines. The results showed that FDA was superior to PCA and PLS-DA in classifying oak-barrel and stainless steel tanks with oak chips aged wines. As a general conclusion, three-dimensional fluorescence spectroscopy can provide valuable fingerprint information for the identification of oak-barrel and stainless steel tanks with oak chips aged wines, while the study will provide some theoretical references and standards for the quality control and quality assessment of oak-barrel aged wines.

The time involved and the high economic cost of using oak barrels to age wines have led to a significant price difference compared to non-oak barrel aged wines and may lead to some fraudulent sales in the market. In this study, an untargeted metabolomic strategy was developed to detect the metabolite composition of oak barrel aged and non-oak barrel aged wines in both positive and negative ion modes by using UHPLC–HRMS combined with the recently developed chemometric method AntDAS. The results of partial least squares discrimination analysis (PLS-DA) showed that the samples were characterized into two categories. Finally, 51 compounds were identified in positive ion mode, while 26 compounds were identified in negative ion mode. The results indicate that combining UHPLC–HRMS with AntDAS can reveal the material basis of wines and has excellent potential to differentiate between oak barrel aged and non-oak barrel aged wines.

This study focuses on the Helan Mountain East Foothills region of Ningxia, a typical continental climate wine-growing area, with Cabernet Sauvignon grapes as the subject. It combines trimethylsilyl derivatization–Gas Chromatography–Mass Spectrometry (TMS-GC-MS) technology and the independently developed AntDAS-GCMS data analysis platform. The aim was to systematically characterize the temporal dynamics of primary and secondary polar metabolites throughout the entire growth cycle of Cabernet Sauvignon in this region. Results identified 50 metabolites exhibiting significant differences (fold change ≥1, p < 0.05) across growth stages, primarily comprising organic acids (18), sugars (7), and amino acids (13). Metabolite accumulation demonstrated distinct stage-specific patterns: organic acids (e.g., tartaric acid, malic acid) peaked before veraison and then declined significantly, while sugars (e.g., fructose) exhibited a marked increase in abundance during the late maturation stage. The underlying mechanisms of the relevant metabolic pathways require further validation through multi-omics approaches. This study elucidates the dynamic characteristics of primary and secondary metabolites throughout the entire growth stages of Cabernet Sauvignon in the region of Ningxia. It provides data support for understanding the metabolic basis of flavor development in grapes from this area and offers practical references for quality regulation and harvest timing optimization in local grape cultivation management.

Goji berries (Lycium barbarum L.) are valued for their nutritional and medicinal properties; however, the systematic biochemical impact of drying on their quality remains poorly understood. This study applied an untargeted metabolomics approach based on UHPLC-HRMS and AntDAS to profile metabolic changes during sun-drying. Multivariate analyses (PCA and PLS-DA) revealed distinct time-dependent clustering, indicating significant shifts in the metabolome. Key metabolites, including betaine, galactose, and trans-ferulic acid, increased significantly (p < 0.05), whereas choline, allantoin, and huperzine isomers decreased. Pathway analysis highlighted glycine, serine, threonine, galactose, and phenylpropanoid metabolism as the central pathways that were affected. These differential metabolites could potentially be used as quality biomarkers. Our findings establish untargeted metabolomics as an effective tool for elucidating the evolution of goji berry quality during drying, offering a theoretical basis for process optimization.