Advancing smart detection of pesticide residues in food through machine learning-enhanced vibrational spectroscopy techniques.

Abstract

The widespread use of over 1000 pesticides globally has raised significant concerns regarding their residues in food, which can pose substantial health risks through consumption or environmental exposure. Traditional methods such as high-performance liquid chromatography (HPLC) and gas chromatography-mass spectrometry (GC-MS), while effective, are often time-consuming, labor-intensive, and require specialized equipment, prompting the need for more rapid, non-destructive, and cost-effective alternatives. This review explores recent breakthroughs in the smart detection of pesticide residues in food, focusing on the integration of vibrational spectroscopy techniques, specifically MIR, NIR, Raman and surface-enhanced Raman spectroscopy (SERs) with machine learning (ML). The application of ML enhances the diagnostic power of vibrational spectroscopy by enabling automated, user-friendly analysis that eliminates the need for complex spectral interpretation. Furthermore, recent innovations in nanomaterial innovations, smartphone assistants, open-AI analysis, and miniaturization techniques significantly advanced the accessibility, precision, and operational efficiency of pesticide detection. These developments not only improve the accuracy and speed of pesticide residue screening but also pave the way for smart, portable, and widely applicable solutions to enhance food safety and safeguard public health.