Simultaneous spectrophotometric determination of four preservatives in foodstuffs by multivariate calibration and artificial neural networks  

作　　者：Yan Qing Chen Yong Nian Ni 

机构地区：[1]State Key Laboratory of Food Science and Technology, Nanchang University, Nanchang 330047, China [2]Department of Chemistry, Nanchang University, Nanchang 330031, China [3]Experimental center of Basic Chemistry, Nanchang Hangkong University, Nanchang 330063, China

出　　处：《Chinese Chemical Letters》2009年第5期615-619,共5页中国化学快报（英文版）

基　　金：the financial support of this study by the National Natural Science Foundation of China(No.20562009);the State Key Laboratory of Food Science and Technology of Nanchang University(No.SKLF-TS-200819 and -MB-200807);the Jiangxi Province Natural Science Foundation(No.JXNSF0620041);the program for Changjiang Scholars and Innovative Research Team in Universities(No.IRT0540).

摘　　要：Benzoic acid （BA）, methylparaben （MP）, propylparaben （PP） and sorbic acid （SA） are food preservatives, and they have well defined UV spectra. However, their spectra overlap seriously, and it is difficult to determine them individually from their mixtures without preseparation. In this paper, seven different chemometric approaches were applied to resolve the overlapping spectra and to determine these compounds simultaneously. With respect to the criteria of % relative prediction error （RPE） and % recovery, principal component regression （PCR） and radial basis function-artificial neural network （RBF-ANN） were the preferred methods. These two methods were successfully applied to the analysis of some commercial samples.Benzoic acid （BA）, methylparaben （MP）, propylparaben （PP） and sorbic acid （SA） are food preservatives, and they have well defined UV spectra. However, their spectra overlap seriously, and it is difficult to determine them individually from their mixtures without preseparation. In this paper, seven different chemometric approaches were applied to resolve the overlapping spectra and to determine these compounds simultaneously. With respect to the criteria of % relative prediction error （RPE） and % recovery, principal component regression （PCR） and radial basis function-artificial neural network （RBF-ANN） were the preferred methods. These two methods were successfully applied to the analysis of some commercial samples.

关 键 词：SPECTROPHOTOMETRY PRESERVATIVES Multivariate calibration Artificial neural networks 

分 类 号：O657.32[理学—分析化学] TP183[理学—化学]