School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China.
School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland.
Food Chem. 2017 Mar 1;218:543-552. doi: 10.1016/j.foodchem.2016.09.051. Epub 2016 Sep 13.
Surface-enhanced Raman scattering (SERS) imaging coupling with multivariate analysis in spectral region of 200 to 1800cm was developed to quantify and visualize thiophanate-methyl (TM) and its metabolite carbendazim residues in red bell pepper (Capsicum annuum L.). Least squares support vector machines (LS-SVM) and support vector machines (SVM) models based on seven optimized characteristic peaks that showed SERS effects of TM and its metabolite carbendazim residues were employed to establish prediction models. SERS spectra with first derivative (1st) and second derivative (2nd) method were subsequently compared and the optimized model of 1st-LS-SVM acquired showed the best performance (RPD=6.08, R=0.986 and RMSEP=0.473). The results demonstrated that SERS imaging with multivariate analysis had the potential for rapid determination and visualization of the trace TM and its metabolite carbendazim residues in complex food matrices.
表面增强拉曼散射（SERS）成像与光谱区域 200 至 1800cm 的多元分析相结合，用于定量和可视化辣椒（Capsicum annuum L.）中的噻菌灵（TM）及其代谢物多菌灵残留。基于七个具有 SERS 效应的 TM 和其代谢物多菌灵残留特征峰的最小二乘支持向量机（LS-SVM）和支持向量机（SVM）模型，建立了预测模型。随后比较了一阶导数（1st）和二阶导数（2nd）方法的 SERS 光谱，获得的优化 1st-LS-SVM 模型表现出最佳性能（RPD=6.08，R=0.986 和 RMSEP=0.473）。结果表明，多元分析的 SERS 成像具有快速测定和可视化复杂食品基质中痕量 TM 及其代谢物多菌灵残留的潜力。
