1：Experimental Design and Method Selection:

The study designed a sensor array with six elements using modified UCNPs (UCNPs@GDN, UCNPs@SO3H, UCNPs@PO(OH)2, and their mixtures) to interact with red grape wine components, utilizing fluorescence quenching as the detection mechanism. Principal component analysis (PCA) was employed for data analysis to differentiate wines based on fluorescence response patterns.

2：Sample Selection and Data Sources:

Red grape wines from nine different manufacturers (details in Table S2) were used as samples. Wine ingredients such as tannic acid were tested to understand the quenching effects.

3：List of Experimental Equipment and Materials:

Equipment included transmission electron microscopy (TEM, 2010 FEF, JEOL), X-ray diffractometer (Siemens D5005, Bruker), FT-IR spectrophotometer (Vector 22, Bruker), fluorescence spectrometer (F-2500, Hitachi), zeta potentiometer (ZS 90, Malvern), and a 980 nm laser (Beijing Viasho Technology Co.). Chemicals were sourced from TCI chemical, Sigma Aldrich, J&K chemical, Alfa aesar, GFCO chemical, and Sinopharm chemical reagent Co. Ltd.

4：Experimental Procedures and Operational Workflow:

UCNPs were synthesized via solvothermal method and modified with functional groups. Fluorescence responses were measured by mixing fluorescent materials with wine samples or ingredients in specific buffers (HEPES pH 7.0, PBS pH 9.0, Tris–HCl pH 6.0), incubating for 3 minutes, and recording intensity at 551 nm with 980 nm excitation. Optimization included pH, concentration, and incubation time tests.

5：0, PBS pH 0, Tris–HCl pH 0), incubating for 3 minutes, and recording intensity at 551 nm with 980 nm excitation. Optimization included pH, concentration, and incubation time tests. Data Analysis Methods:

5. Data Analysis Methods: PCA was used to analyze fluorescence-response patterns, reducing dimensionality to differentiate wines based on variance in sensor element responses.