Development of a Universal Model for In Situ Detection of Pymetrozine Residues on Cabbage and Chinese Cabbage by Surface-Enhanced Raman Spectroscopy

Current calibration models based on surface-enhanced Raman spectroscopy (SERS) spectra data for pesticide residue detection in foods demonstrate excellent species-specific performance, yet their cross-species applicability remains challenging. This study aimed to build a universal model suitable for in situ detection of pymetrozine residues on two different vegetable species. SERS spectra of cabbage and Chinese cabbage leaves contaminated with pymetrozine were collected, and species-specific and universal models were constructed based on the spectral data using the partial least-squares regression algorithm. The results suggested that species-specific models demonstrated strong predictive performance for estimating the pymetrozine residue concentrations on a specific vegetable but limited accuracy when applied to another vegetable species. However, the universal model exhibited excellent performance for both vegetable species, with an correlation coefficient of prediction set (<i>R</i>_p²) and root-mean-square error of prediction (RMSEP) values of 0.9637 and 0.6225 mg/L for cabbage and 0.9621 and 0.6362 mg/L for Chinese cabbage, respectively. The findings of the study indicate that the universal model is suitable for in situ detection of pymetrozine residue concentrations on two different vegetable species.