应用化学2025，Vol.42Issue(11)：1510-1523,14.DOI:10.19894/j.issn.1000-0518.250115
基于表面增强拉曼光谱结合深度学习模型快速定量检测菠菜中的氯氰菊酯
Rapid and Quantitative Detection of Cypermethrin in Spinach Based on Surface-Enhanced Raman Spectroscopy Combined with Deep Learning Model
摘要
Abstract
The residues of pyrethroid pesticides in vegetables and fruits can cause harm to human health.In this work,a surface-enhanced Raman spectroscopy(SERS)coupled with back propagation(BP)neural network deep learning model was established for rapid detection of cypermethrin(CPM)in spinach.Concretely,Ag/ZnO were applied as SERS substrates.Then,the collected SERS spectra were expanded,and the expanded spectral data were preprocessed by Savitzky-Golay(S-G),mean centralization(MC)and the combination of the two methods.Furthermore,BP neural network models were established for the prediction of CPM in actual samples for the original enhanced spectrum and the pretreated spectrum,respectively,and three traditional machine learning models were established for comparison.It was found that the BP model has the best prediction performance based on the original expanded spectrum,with the prediction set RP=0.9902,the root mean square error RMSEP=0.102 and the limit of detection 20 µg/L.The two-tailed paired t-tests showed that there was no significant difference between the standard method LC-MS and this method.The detection can be finished in 5～10 min.This work established an Ag/ZnO-based SERS method coupled with BP neural network model for the rapid and quantitative detection of CPM residues in spinach.关键词
氯氰菊酯/菠菜/表面增强拉曼光谱/深度学习/反向传播神经网络Key words
Cypermethrin/Spinach/Surface-enhanced Raman spectroscopy/Deep learning/Back propagation neural network分类
化学化工引用本文复制引用
吴秀秀,刘志敏,杨栋,毛顺,王焱鑫,郭德华,徐斐..基于表面增强拉曼光谱结合深度学习模型快速定量检测菠菜中的氯氰菊酯[J].应用化学,2025,42(11):1510-1523,14.基金项目
上海市"人工智能促进科研范式改革赋能学科跃升计划"项目(No.Z-2025-312-023)资助 Supported by Program of Shanghai Artificial Intelligence to Promote the Reform of Scientific Research Paradigm Enabling Discipline Leaping Plan(No.Z-2025-312-023) （No.Z-2025-312-023）
