Fluorescence nanomaterial sensors have exhibited excellent application potential in biothiols analyses. The fluorescence sensor arrays constructed from upconversion luminescence metal-organic frameworks nanocomposites (LMOFs) can provide impressive discrimination and exquisite fingerprinting capabilities for extremely similar analytes. Herein, an upconversion fluorescence sensor array based on LMOFs featuring UiO-type metal-organic frameworks-functionalized lanthanide-doped upconversion nanoparticles was proposed, wherein Cu2+ can make the fluorescence quenching of LMOFs and preferentially bind biothiols to recover fluorescence in different degrees forming unique fingerprinting. The fluorescence sensor array displayed an excellent pattern recognition for five biothiols (glutathione, homocysteine, N-acetylcysteine, and L/D-cysteine) even at 50 µM by linear discriminant analysis, and the discernment for the enantiomers of L/D-cysteine, as well as the accurate identification (90.0% accuracy) of biothiols in food samples (tea beverage and white wine). Such fluorescence sensor array might provide a simple and efficient detection method for biothiols.

Zhalajiao, a traditional Chinese fermented food, is popular due to its unique flavor. Traditional Zhalajiao fermentation is closely related to flavor compounds production. However, the mechanisms underlying the formation of these crucial flavor components in Zhalajiao remain unclear. Here, we explored the dynamic changes in physical and chemical properties, microbial diversity, and flavor components of Zhalajiao at various fermentation times. In total, 6 organic acids, 17 amino acids, and 21 key volatile compounds were determined as flavor components. In Zhalajiao, Lactobacillus and Cyanobacterium were the main bacteria that were involved in the formation of crucial flavor compounds. Candida showed a significant correlation with 14 key flavor compounds during fermentation (p < 0.05) and was the main fungal genus associated with flavor formation in Zhalajiao. This research offers a theoretical foundation for the flavor regulation and quality assurance of Zhalajiao.

Herein, the miniaturized thermal purge-and-trap (MTPT) device combined with self-calibration colorimetric/surface-enhanced Raman spectroscopy (SERS) dual-model optical sensors were designed for effective analysis of sulfur dioxide (SO₂) in wine. The SO₂ can be rapidly separated from wine and enriched by MTPT device, ensuring colorimetric/SERS dual-model optical sensing based on Karl Fischer reaction. The high separation efficiency of miniaturized MTPT device combined with self-calibration of dual-model optical sensors significantly alleviate matrix interference and improve the detection accuracy. The satisfactory linear range of 0.1–200.0 mg/L and 0.1–500.0 mg/L with limit of detection of 0.03 mg/L can be obtained. Finally, the MTPT-colorimetric/SERS method was applied to determine the content of SO₂ in different kinds of wine to verify the practicality. These results provide an ideal strategy in construction of MTPT device combined with self-calibration dual-model optical sensors for quantification of gaseous hazards in complex food samples with high rapidity, anti-interference and accuracy.

To reveal the characteristic physicochemical indicators of wines of different quality grades and explore their feasibility as auxiliary indicators for grading, 23 wines from the Manas subregion of Xinjiang were used as test materials. Sensory evaluation, colour difference analysis, and electronic tongue technology were employed, combined with nontargeted metabolomics and quantitative analysis, to analyze differences in phenolic compounds, colour parameters, and taste characteristics among wines of different grades. Finally, a quality evaluation model for Cabernet Sauvignon wine was constructed using partial least squares regression (PLSR). The results revealed significant differences in the L* values, a* values, and C*ab values among wines of different grades. Grade A wines presented lower L* values, higher a* values, and higher C*ab values, indicating lower brightness, deeper red tones, and higher saturation. Taste characteristic differences were primarily manifested in Grade A wines, which have higher acidity, astringency, bitterness, and richness but exhibit lower bitterness aftertaste and astringency aftertaste. The results of the quantitative analysis and correlation analysis indicate that the differences in sensory characteristics among different grades of wine stem from variations in their polyphenolic compound contents. The higher anthocyanin content in Grade A wine is associated with higher a* values; higher flavonoid content is closely related to higher astringency and bitterness values; and lower flavanol content is associated with lower bitterness aftertaste and astringency aftertaste values. The PLSR model results indicate that when sensory characteristic parameters and phenolic compound content are used as predictor variables (X) and grade is used as the response variable (Y), the PLSR model has a calibration set R2 = 0.97 and a validation set R2 = 0.92, the calibration set RMSE is 0.13, and the validation set RMSE is 0.25. The model demonstrates good fitting performance, establishing an objective method for evaluating wine quality that avoids evaluation errors caused by the subjective factors of winemakers and tasters. This study is the first to conduct a comprehensive evaluation of the sensory characteristic and chemical components of three grades of wine, providing data support and theoretical references for the improvement of wine quality evaluation systems.