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近红外光谱法鉴别奶牛饲料中三聚氰胺甲醛树脂的可行性研究
引用本文:刘星,单杨,张欣,杨桂森.近红外光谱法鉴别奶牛饲料中三聚氰胺甲醛树脂的可行性研究[J].食品科学,2012,33(12):154-158.
作者姓名:刘星  单杨  张欣  杨桂森
作者单位:1.中南大学研究生院隆平分院 2.湖南省食品测试分析中心 3.湖南省农产品加工研究所 4.中国科学院上海应用物理研究所
基金项目:“十一五”国家科技支撑计划项目(2009BADB7B07);中南大学学位论文创新资助项目(2010ssxt256)
摘    要:收集国内常用的、具有代表性的奶牛精补料33个样品,制备99个三聚氰胺甲醛树脂(MF)掺假样品,在全光谱范围内进行近红外透反射光谱扫描,选择合适的前处理方法,采用BP神经网络方法和PLS-LDA方法分别建立判别模型。建立的BP神经网络判别分析模型的预测正确率为100%,建立的PLS-LDA判别分析模型的交互验证最低错误率为0.0778,模型错分率为0.0667,模型预测错误率为0.1429。说明利用近红外透反射光谱建立定性分析模型来检测奶牛饲料中是否掺有MF的研究是可行的。

关 键 词:奶牛饲料  三聚氰胺甲醛树脂  近红外光谱  BP神经网络  PLS-LDA  

Feasibility Study of Identifying Melamine-Formaldehyde Resin in Cow Feed by Near Infrared Spectroscopy
LIU Xing,SHAN Yang,ZHANG Xin,YANG Gui-sen.Feasibility Study of Identifying Melamine-Formaldehyde Resin in Cow Feed by Near Infrared Spectroscopy[J].Food Science,2012,33(12):154-158.
Authors:LIU Xing  SHAN Yang  ZHANG Xin  YANG Gui-sen
Affiliation:(1. Longping Branch, Graduate School, Central South University, Changsha 410125, China;2. Hunan Food Testing and Analysis Center, Changsha 410125, China;3. Hunan Agricultural Product Processing Institute, Changsha 410125, China; 4. Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China)
Abstract:Thirty-three samples of typical cow feed concentrate supplement without melamine-formaldehyde resin(MFR) and 99 ones adulterated with different concentrations of MFR were subjected to full wavelength near-infrared transmission and reflectance spectroscopic scanning.A discrimination model was established by BP(back propagation) neural network or PLS-LDA(partial least squares-linear discriminant analysis).The BP neural network model showed 100% accurate prediction.The minimum cross validation error rate of the PLS-LDA model was 0.0778,misclassification rate 0.0667,and prediction error rate 0.1429.Therefore,it is feasible to establish a predictive model for detecting MFR adulteration in cow feed by means of near-infrared transmission and reflectance spectroscopy.
Keywords:cow feed  melamine-formaldehyde resin  near infrared spectroscopy  back propagation(BP) neural network  partial least squares-linear discriminant analysis(PLS-LDA)
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