Targeted Detection of Low-Level Chlorpyrifos Residues in Milk Using Chemometrics-Aided Raman Spectroscopy

Contamination of milk with pesticide residues such as chlorpyrifos poses serious public health concerns, necessitating highly sensitive analytical tools for effective monitoring. Raman spectroscopy offers a non-invasive and practical detection method; however, conventional full-spectrum or broad region analyses often fail to reliably detect low-level residues due to spectral overlap and matrix interference. This study demonstrates that surface enhanced Raman spectroscopy, combined with chemometric techniques (ANOVA and PCA), can establish a selective molecular fingerprint for chlorpyrifos, enabling accurate detection even at concentrations below the maximum residue limit (MRL) of 0.01 ppm. Spectra were acquired from both pesticide-free milk and samples spiked at various chlorpyrifos concentrations. ANOVA identified statistically significant spectral differences at 342, 572, 634, 736, 1378, and 1568 cm−1, with the greatest variance observed in the 314–750 cm−1 range. PCA revealed that the 314–354 cm−1 band, centered at 342 cm−1 and corresponding to the C–Cl stretching mode unique to chlorpyrifos, served as the most reliable molecular fingerprint. This band was absent in the milk matrix, allowing clear differentiation across contamination levels. These findings support a targeted fingerprinting strategy that minimizes spectral interference and enhances sensitivity for trace-level detection, offering a rapid, robust, and cost-effective tool for pesticide monitoring in milk. The method holds promise for broader application in food safety surveillance and regulatory compliance.