SELECTIVE DETECTION AND QUANTITATIVE ANALYSIS METHOD FOR SALICYLIC ACID BASED ON Ag@UiO-66 MATERIAL USING SURFACE-ENHANCING RAMAN FAR-DISCHARGE SPECTROSCOPY (SERS) COMBINED WITH MACHINE LEARNING

Abstract

The invention proposes a method for the selective detection and quantitative analysis of salicylic acid based on Ag@UiO-66 material using surface-enhanced Raman scattering (SERS) spectroscopy combined with machine learning. The process includes the following steps: a) fabrication of Ag nanomaterials, b) fabrication of Ag-UiO-66 materials, c) harvesting of Ag@UiO-66 materials, d) fabrication of sensor substrates and SERS spectral measurement, and e) quantitative processing and identification using machine learning. This process is implemented through a solvent-activated self-assembly method at room temperature to form a multi-core-porous Ag@UiO-66 composite structure combined with a machine-learning-assisted molecular identification mechanism. A machine learning-assisted analytical framework supports the processing, analysis, and identification of spectral data from complex sample matrices, thereby enabling selective and accurate identification of the analyte.