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In this work,artificial neural network(ANN),a powerful chemometrics approach for linear and non-linear 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.
In this work, artificial neural network (ANN), a powerful chemometrics approach for linear and non-linear 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.