期刊
LWT-FOOD SCIENCE AND TECHNOLOGY
卷 97, 期 -, 页码 760-769出版社
ELSEVIER
DOI: 10.1016/j.lwt.2018.07.055
关键词
Chlorpyrifos; Surface-enhanced Raman spectroscopy (SERS); Chemometrics; Nanoparticle; Tea samples
资金
- National Key Research and Development Program of China [2017YFD0400800]
- Key RAMP
- D Program of Jiangsu Province [BE2017357]
- National Key RAMP
- D Program of China [2017YFC1600801]
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 (slope = 0.98-1.00), higher correlation coefficient of determination (r(2) = 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.
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