Determination of carbaryl pesticide in Fuji apples using surface-enhanced Raman spectroscopy coupled with multivariate analysis

Abstract

Residual pesticides in fruits and vegetables are one of the major food safety concerns around the world. Surface-enhanced Raman spectroscopy (SERS) coupled with chemometric methods was applied for quantitative analysis of trace levels of carbaryl pesticide in apple. The lowest detectable level for carbaryl in apple was 0.5μgg-1, which was sensitive enough for identifying apple contaminated with carbaryl above the maximum residue level. Quantification of carbaryl residues (0-10μgg-1) was conducted using partial least squares regression (PLSR) and support vector regression (SVR) models. Based upon the results of leave-one-out cross-validation, carbaryl levels in apples could be predicted by PLSR (R2 = 0.983) or SVR (R2 = 0.986) with a low root mean square errors (RMSE = 0.48μgg-1 or 0.44μgg-1) and a high ratio of performance to deviation (RPD = 7.71 or 8.11) value. This study indicates that SERS has the potential to quantify carbaryl pesticide in complex food matrices reliably.