Surface-enhanced Raman spectroscopy (SERS) combined with chemometric models were employed to develop a rapid, low-cost, and sensitive method for qualitative and quantitative analysis of chlorpyrifos residues in tea. [email protected] nanoparticles (NPs) with high enhancement factor were synthesized and coupled with chemometric algorithms for SERS measurements. K-nearest neighbors (KNN) classification models gave the best performance model with high classification rates (90.84–100.00%) achieved. For the quantification models for predicting chlorpyrifos contents, the genetic algorithm-partial least squares (GA-PLS) models and synergy interval partial least squares-genetic algorithm (siPLS-GA) models applied to standard normal variate transformation (SNV) preprocessed training and validation data set showed better prediction performances with excellent regression quality (slope = 0.98–1.00), higher correlation coefficient of determination (r² = 0.96–0.98), and lower root-mean-square error of prediction (RMSEP = 0.29, 0.31) than other quantification models. Paired sample t-test exhibited no statistically significant difference between the reference values determined by GC-MS and the predicted values in most quantification models. The proposed method would be a more effective and powerful tool for classification and determination of chlorpyrifos (CPS) residues in tea samples.

采用表面增强拉曼光谱（SERS）和化学计量学模型相结合的方法，开发了一种快速，低成本，灵敏的方法，用于定性和定量分析茶叶中毒死rif的残留量。合成了具有高增强因子的[受电子邮件保护的]纳米颗粒（NP），并与用于SERS测量的化学计量学算法相结合。K近邻（KNN）分类模型提供了具有最高分类率（90.84–100.00％）的最佳性能模型。对于预测毒死rif含量的量化模型，²  = 0.96-0.98），且预测的均方根误差（RMSEP = 0.29，0.31）比其他量化模型低。配对样品的t检验显示，通过GC-MS测定的参考值与大多数定量模型中的预测值之间没有统计学上的显着差异。所提出的方法将是对茶叶样品中毒死rif（CPS）残留物进行分类和测定的更有效和强大的工具。