Li Baoxin, He Yuezhen, Xu Chunli
Department of Chemistry, School of Chemistry and Materials Science, Shaanxi Normal University, Xi'an 710062, PR China.
Talanta. 2007 Apr 15;72(1):223-30. doi: 10.1016/j.talanta.2006.10.023. Epub 2006 Nov 20.
In this article, a continuous-flow chemiluminescence (CL) system with artificial neural network calibration is proposed for simultaneous determination of three organophosphorus pesiticides residues. This method is based on the fact that organophosphorus pesticides can be decomposed into orthophosphate with potassium peroxodisulphate as oxidant under ultraviolet radiation and that the decomposing kinetic characteristics of the organophosphorus pesticides with different molecular structure are significantly different. The produced orthophosphate can react with molybdate and vanadate to form the vanadomolybdophosphoric heteropoly acid, which can oxidize luminol to produce intense CL emission. The CL intensity of the solution was measured and recorded every 2s in the range of 0-250s. The obtained data were processed chemometrically by use of a three-layered feed-forward artificial neural network trained by back-propagation learning algorithm, in which input node, hidden node and output nodes were 65, 21 and 3, respectively. The proposed multi-residue analysis method was successfully applied to the simultaneous determination of the three organophosphorus pesticides residue in some vegetables samples.
本文提出了一种基于人工神经网络校准的连续流动化学发光(CL)系统,用于同时测定三种有机磷农药残留。该方法基于以下事实:在紫外光辐射下,以过二硫酸钾为氧化剂,有机磷农药可分解为正磷酸盐,且不同分子结构的有机磷农药的分解动力学特征存在显著差异。生成的正磷酸盐可与钼酸盐和钒酸盐反应形成钒钼磷杂多酸,该杂多酸可氧化鲁米诺产生强烈的化学发光发射。在0 - 250s范围内,每2s测量并记录一次溶液的化学发光强度。利用反向传播学习算法训练的三层前馈人工神经网络对获得的数据进行化学计量学处理,其中输入节点、隐藏节点和输出节点分别为65、21和3。所提出的多残留分析方法成功应用于同时测定一些蔬菜样品中的三种有机磷农药残留。