Simultaneous voltammetric determination of four triazine herbicides in water samples with the aid of chemometrics

A novel differential pulse voltammetry (DPV) method was developed for the simultaneous analysis of herbicides in water. A mixture of four herbicides, atrazine, simazine, propazine and terbuthylazine was analyzed simultaneously and the complex, overlapping DPV voltammograms were resolved by several chemometrics methods such as partial least squares (PLS), principal component regression (PCR) and principal component–artificial networks (PC–ANN). The complex profiles of the voltammograms collected from a synthetic set of samples were best resolved with the use of the PC–ANN method, and the best predictions of the concentrations of the analytes were obtained with the PC-ANN model (%RPE_T = 6.1 and average %Recovery = 99.0). The new method was also used for analysis of real samples, and the obtained results were compared well with those from the GC-MS technique. Such conclusions suggest that the novel method is a viable alternative to the other commonly used methods such as GC, HPLC and GC-MS.     

Simultaneous determination of three herbicides by differential pulse voltammetry and chemometrics

A novel differential pulse voltammetry method (DPV) was researched and developed for the simultaneous determination of Pendimethalin, Dinoseb and sodium 5-nitroguaiacolate (5NG) with the aid of chemometrics. The voltammograms of these three compounds overlapped significantly, and to facilitate the simultaneous determination of the three analytes, chemometrics methods were applied. These included classical least squares (CLS), principal component regression (PCR), partial least squares (PLS) and radial basis function-artificial neural networks (RBF-ANN). A separately prepared verification data set was used to confirm the calibrations, which were built from the original and first derivative data matrices of the voltammograms. On the basis relative prediction errors and recoveries of the analytes, the RBF-ANN and the DPLS (D - first derivative spectra) models performed best and are particularly recommended for application. The DPLS calibration model was applied satisfactorily for the prediction of the three analytes from market vegetables and lake water samples.     

Nylon 6,6 modified screen printed carbon electrodes as electrochemical sensors for rapid chlorothalonil determination in water samples using differential pulse cathodic stripping voltammetry

A newly developed electrochemical sensor for chlorothalonil based on nylon 6,6 film deposited onto screen printed electrode (SPE) with electrochemical modulation of pH at the electrode/solution interface was studied for the first time. Differential pulse cathodic stripping voltammetry (DPCSV) was used to carry out the electrochemical and analytical studies. Experimental parameters such as accumulation potential, initial potential, accumulation time and pH of Britton-Robinson buffer have been optimized. Chlorothalonil gave optimum analytical signal in a medium of 0.04?M Britton-Robinson buffer at pH 6.0. A well-defined reduction peak was observed, at E_p= ?0.851 and ?0.938?V vs. Ag/AgCl (3.0?M KCl) for both bare SPE and modified SPE, respectively. The peak currents of modified SPE were significantly increased as compared to bare SPE. At the modified SPE, a linear relationship between the peak current and chlorothalonil concentration was obtained in the range from 0.1 to 2.8?×?10^?6?M with a detection limit of 1.53?×?10^?8?M (S/N=?3). The practical applicability of the newly developed method has been demonstrated on analyses of real water samples. The newly developed sensor shows good reproducibility with RSD of 3.92%. The nylon 6,6 modified SPE showed itself as promising sensor with good selectivity for chlorothalonil determination.     

多元回归和BP人工神经网络在预测混合厌氧消化产气量过程中的应用比较

对脂肪类单基质和城市污水厂剩余污泥混合厌氧消化过程的产气阶段进行基于多元回归和BP人工神经网络的产气量预测模型比较研究。实验数据分别取自反应过程的第1～16天和第17～70天。结果表明:多元回归模型的预测平均准确率分别为75.69%和79.29%;BP神经网络模型的预测平均准确率为79.05%。通过对比2种模型的预测结果可知,两种模型都有较高的预测准确率,但BP模型的预测准确率更高,更适用于混合厌氧消化产气量预测。