SERS-Based Detection of Food Contaminants: From Laboratory Sensitivity to Practical Implementation-Bottlenecks and...

Ensuring food safety requires the detection of trace-level contaminants such as pesticides, mycotoxins, and heavy metals. Analytical approaches for these analytes should feature high sensitivity, good selectivity, and compatibility with aqueous matrices; surface-enhanced Raman spectroscopy (SERS) satisfies these requirements. Addressing the absence of a unified comparative analytical framework, this critical review surveys recent SERS-enabled sensing strategies for food contaminants. Detection strategies differ substantially across the three contaminant classes: pesticides can be directly detected at ppb levels through substrate engineering and deep learning; mycotoxins rely on affinity-recognition elements to reach pg-mL-level sensitivity; and Raman-inactive heavy metals demand indirect readout via functional probes. Crucially, despite these divergent analytical routes, the field confronts three shared bottlenecks-spectral irreproducibility, severe matrix interference, and the lack of standardized protocols, all of which hinder regulatory adoption. Compared with near-infrared spectroscopy (NIR) and hyperspectral imaging (HSI), SERS delivers outstanding sensitivity for confirmatory trace-level analysis, while its limited throughput may be compensated by multispectral data fusion. Future advances should prioritize portable sensing hardware, explainable Artificial Intelligence (AI), and multiplexed detection to transfer laboratory-scale sensitivity toward practical field-deployable testing tools.