The increasing use of pesticides and their mixtures poses a serious risk to human health and the environment. This increases the demand for simple, cost-effective, and reliable methods for detecting these residues. In this study, a highly sensitive in-house SERS platform based on a metal–insulator–metal (MIM) nanoarray structure was employed to acquire Raman fingerprint spectra of Pyrimethanil (PYM), Imidacloprid (IMI), and Chlormequat chloride (CCC) in pepper juice, yielding spectra with high signal-to-noise ratios. The detection limit for PYM in pepper juice (0.16 mg/kg) was well below both the EFSA (2 mg/kg) and EPA (2 mg/kg) limits. Among the tested pesticides, PYM shows the lowest detection limit, indicating a more efficient signal enhancement for the π-metal interaction. This strong affinity results in significantly enhanced Raman scattering activity. Furthermore, the unsupervised machine learning analysis techniques (e.g., PCA and HCA) used showed a concentration-dependent separation in spiked samples. The same approach also enabled detection and discrimination in real food samples obtained from different regions. These results demonstrate the potential of the developed platform for rapid, on-site monitoring of pesticide residues in complex food matrices. • A MIM SERS bowtie nanoarray was developed for the detection of PYM, IMI, and CCC. • The substrate demonstrated matrix compatibility in real and spiked pepper extracts. • The calculated LoD value for PYM (0.161 ppm) remained below the EFSA and EPA limits (2 mg/kg). • Unsupervised machine learning analyses successfully distinguished concentration-dependent variations in pesticides and pepper samples obtained from different regions. • The consistency of the data obtained from controlled contaminated (spiked) and real samples demonstrates the reliability and analytical accuracy of the method used.
Öncer et al. (Sun,) studied this question.