Portable Raman spectroscopy combined with machine learning for highly sensitive and rapid detection of food pollutants with flexible Ag@TiO2@polyester SERS substrates

Abstract

Surface-enhanced Raman spectroscopy (SERS) holds promise as a sensing technique, yet it faces challenges in precisely identifying trace contaminants in food due to limitations in substrate sensitivity and high surface purity. This study presents an SERS substrate enabling the precise and ultrasensitive identification of multiple pollutants combined with machine learning algorithms and a portable Raman spectrometer. The substrate achieves an enhancement factor of up to 1.02 × 10⁸. This enhancement is attributed to the synergistic effects of Ag nanoparticles (NPs) and a porous TiO₂ layer on the substrate. Leveraging its high surface purity and exceptional sensitivity, the substrate successfully distinguishes between multiple hazardous pollutants with similar geometries and ultralow concentrations in aquatic products, aided by principal component analysis (PCA). Consequently, this effective SERS substrate, combined with artificial intelligence, advances the application of SERS technology in accurately identifying trace contaminants in the field of food safety.