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.
A reliable method for simultaneous determination of three antibiotic drugs（levofloxacin,gatifloxacin and lomefloxacin） by differential pulse stripping voltammetry（DPSV） in Britton-Robinson buffer（pH 7.96） was presented.The method is based on adsorptive accumulation of the antibacterial drugs on a hanging mercury dropping electrode（HMDE）,followed by the reduction of the adsorptive species by the technique of DPSV.Optimal conditions,the deposition time of 80 s,the deposition potential of—1250 mV,and the scan rate of 25 mV/s,were obtained.The linear concentration ranges of 0.010-0.080μg/mL were obtained for all these three antibiotic drugs,while the detection limits were 2.38,3.20 and 1.60ng/mL for levofloxacin,gatifloxacin and lomefloxacin,respectively.In this work,chemometrics methods,such as classical least squares（CLS）,partial least squares（PLS）, principle component regression（PCR） and radial basis function-artificial neural networks（RBF-ANN）,were used to quantitatively resolve the overlapping signals.It was found that PCR gave the best results with total relative prediction error（RPE_T） of 7.71%.The proposed method was applied to determine these three drugs in several commercial food samples with spiked method and yielded satisfactory recoveries.
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.
