Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
8890321 | LWT - Food Science and Technology | 2018 | 36 Pages |
Abstract
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. Au@Ag 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 (slopeâ¯=â¯0.98-1.00), higher correlation coefficient of determination (r2â¯=â¯0.96-0.98), and lower root-mean-square error of prediction (RMSEPâ¯=â¯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.
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Authors
Jiaji Zhu, Akwasi Akomeah Agyekum, Felix Y.H. Kutsanedzie, Huanhuan Li, Quansheng Chen, Qin Ouyang, Hui Jiang,