Integrated SERS and Fluorescence Enhancement Strategies for Trace Contaminants in Food.

Abstract

Trace contaminants in food pose substantial health risks, yet their spectroscopic detection remains challenging because low analyte abundance and complex matrices cause signal suppression, background interference, and poor quantitative robustness. This structured narrative review synthesizes peer-reviewed studies published from January 2019 to June 2026 and retrieved from Web of Science, PubMed, and ScienceDirect on integrated detection strategies based on surface-enhanced Raman spectroscopy (SERS) and fluorescence spectroscopy (FS). Integrated enhancement is defined as the functional coupling of recognition, enrichment, matrix cleanup, signal transduction, optical amplification, and data interpretation within an analytical workflow. Based on the functional relationships among these modules, strategies are classified as sequential, parallel, or hybrid, while artificial intelligence (AI)-assisted interpretation is treated as a cross-cutting data-analysis layer rather than an independent enhancement mode. Applications involving pesticide residues, mycotoxins and biogenic amines, veterinary drug residues, illicit additives, adulterants and banned residues, and heavy metals are compared in terms of matrix-interference control, analytical robustness, validation depth, portability, and regulatory relevance. Sequential enhancement is most suitable when cleanup, enrichment, indirect transduction, or staged amplification is required; parallel enhancement favors rapid, ratiometric, multichannel, and portable detection; and hybrid enhancement combines upstream purification with downstream multimodal signal generation. AI-assisted interpretation is particularly valuable when spectral overlap, nonlinear responses, or matrix-dependent backgrounds limit direct analysis. Current studies remain constrained by inconsistent validation, overreliance on detection limits, limited external testing of AI models, and inadequate standardization of substrate and probe reproducibility. This workflow-oriented framework supports the rational selection and evaluation of integrated spectroscopic strategies.