In this work, artificial neural network （ANN）, a powerful chemometrics approach for linear and nonlinear calibration models, was applied to detect three pesticides in mixtures by linear sweep stripping voltammetry ...In this work, artificial neural network （ANN）, a powerful chemometrics approach for linear and nonlinear calibration models, was applied to detect three pesticides in mixtures by linear sweep stripping voltammetry （LSSV） despite their overlapped voltammograms. Electrochemical parameters for the voltammetry, such as scan rate, deposit time and deposit potential, were evaluated and optimized from the signal response data using ANN model by minimizing the relative prediction error （RPE）. The proposed method was successfully applied to the detection of pesticides in synthetic samples and several commercial fruit samples.展开更多
基金support by the National Natural Science Foundation of China(No.21065007)the State Key Laboratory of Food Science and Technology of Nanchang University(Nos.SKLF-MB-201002 and SKLF-TS-200919)Jiangxi Province Education Department Science Foundation(No. GJJ10037)
文摘In this work, artificial neural network （ANN）, a powerful chemometrics approach for linear and nonlinear calibration models, was applied to detect three pesticides in mixtures by linear sweep stripping voltammetry （LSSV） despite their overlapped voltammograms. Electrochemical parameters for the voltammetry, such as scan rate, deposit time and deposit potential, were evaluated and optimized from the signal response data using ANN model by minimizing the relative prediction error （RPE）. The proposed method was successfully applied to the detection of pesticides in synthetic samples and several commercial fruit samples.
