Leafy vegetables are consumed in most daily diets worldwide. As living standards improve, food quality, safety requirements, and nutrition are becoming increasingly important to consumers when purchasing leafy vegetables. This study proposes an evaluation and traceability method that can be used to track the nutritional quality of leafy vegetables. Employing the principles of the Hazard Analysis and Critical Control Point (HACCP) system combined with fault tree analysis (FTA), a traceability model for the entire production and sale process of leafy vegetables is constructed. Four common leafy vegetables, spinach, rape, lettuce, and celery are examined in this research to establish a nutritional quality index system using fuzzy mathematics subordinate function method to evaluate nutritional quality. A nutritional quality and safety traceability system based on browser/server architecture and quick response (QR) code is then designed and developed for full traceability of leafy vegetable quality. This method can ensure food safety and hygiene through the control of key factors affecting food safety throughout the entire supply chain process.

Wine astringency is a dynamic oral sensation that remains difficult to predict from chemical composition, particularly under substantial inter-individual variability. Here, a data-driven framework was developed to link wine phenolic composition with dynamic astringency perception. Fifteen phenolic compounds were quantified by HPLC, and six descriptors were derived from time–intensity sensory evaluation. An interpretable Transformer model, Astriformer, was constructed, in which panelist identity was incorporated as a learnable embedding to model individual-specific sensory variation. The model achieved stable prediction of multidimensional astringency and outperformed one-hot encoding and tree-based baselines. Results indicated that astringency is better described as an individual-conditioned mapping rather than a universal function. Stratified SHAP analysis revealed structured sensory phenotypes and cluster-dependent chemical importance patterns. In addition to flavan-3-ol-derived compounds, phenolic acids and vanillin were identified as potential modulators. This study provides an interpretable framework for linking phenolic composition to dynamic sensory perception.