Application of artificial neural networks to the determination of pesticides by linear sweep stripping voltammetry

注册 English

Application of artificial neural networks to the determination of pesticides by linear sweep stripping voltammetry

Ping Qiu , Yong-Nian Ni , Serge Kokot

Citation: Ping Qiu, Yong-Nian Ni, Serge Kokot. Application of artificial neural networks to the determination of pesticides by linear sweep stripping voltammetry[J]. Chinese Chemical Letters, 2013, 24(3): 246-248. shu

shu

Application of artificial neural networks to the determination of pesticides by linear sweep stripping voltammetry

通讯作者: Yong-Nian Ni,

摘要: 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.

关键词:

English

Application of artificial neural networks to the determination of pesticides by linear sweep stripping voltammetry

1.
a Key Laboratory of Food Science and Technology, Nanchang University, Nanchang 330047, China;
2.
b Department of Chemistry, Nanchang University, Nanchang 330031, China;
3.
c School of Chemistry, Physics and Mechanical Engineering, Faculty of Science and Engineering, Queensland University of Technology, Brisbane 4001, Australia

Corresponding author: Yong-Nian Ni,

Received Date: 23 November 2012
Available Online: 04 February 2013

Abstract: 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.

Key words:

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