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.
Near-infrared spectroscopy(NIR),which is generally used for online monitoring of the food analysis and production process, was applied to determine the internal quality of toothpaste samples.It is acknowledged that the spectra can be significantly influenced by non-linearities introduced by light scatter,therefore,four data preprocessing methods,including off-set correction, 1st-derivative,standard normal variate(SNV) and multiplicative scatter correction(MSC),were employed before the date analysis. The multivariate calibration model of partial least squares(PLS) was established and then was used to predict the pH values of the toothpaste samples of different brand.The results showed that the spectral date processed by MSC was the best one for predicting the pH value of the toothpaste samples.
