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 (Rp2) 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.