饲料检测 Feed Detection

近红外和中红外光谱技术在快速鉴别豆粕中掺入尿素聚合物的研究

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  • 1. 中国农业科学院饲料研究所, 北京 100081;
    2. 农业部食物与营养发展研究所, 北京 100081
孙丹丹(1989-),女,山东泰安人,硕士研究生,动物营养与饲料科学专业。E-mail:sundandan1227@126.com

收稿日期: 2014-10-24

  网络出版日期: 2015-04-21

基金资助

公益性行业(农业)科研专项经费项目(20120323)

Adulteration Detection of Soybean Meal of Rapid Identification Based on Near Infrared Spectroscopy and Mid Infrared Spectroscopy

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  • 1. Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China;
    2. Institute of Food and Nutrition Development, Ministry of Agriculture, Beijing 100081, China

Received date: 2014-10-24

  Online published: 2015-04-21

摘要

本试验旨在应用并比较近红外和中红外光谱技术结合模式识别方法对掺假的豆粕进行快速鉴别。试验收集了不同批次145个纯豆粕样品,随机选取部分纯豆粕样品,掺入0.08%~5.00%的尿素聚合物,利用傅立叶变换近红外和中红外光谱技术及偏最小二乘判别分析(PLS-DA)和支持向量机(SVM)分类方法,对掺假豆粕进行识别。结果表明:近红外光谱经变量标准化后建立的SVM分类模型训练集识别率为99.8%,测试集识别率为99.2%,检测限为1.0%;中红外光谱数据经变量标准化和一阶导数7点平滑处理后,建立的PLS-DA和SVM分类模型对样品的识别率均达到100.0%,检测限为0.08%。因此,近红外和中红外光谱技术均可对掺入尿素聚合物的豆粕快速鉴别,后者的灵敏度高于前者。

本文引用格式

孙丹丹, 李军国, 秦玉昌, 董颖超 . 近红外和中红外光谱技术在快速鉴别豆粕中掺入尿素聚合物的研究[J]. 动物营养学报, 2015 , 27(4) : 1199 -1206 . DOI: 10.3969/j.issn.1006-267x.2015.04.025

Abstract

This experiment was conducted to study the adulteration detection of soybean meal of rapid identification based on near infrared (NIR) spectroscopy and mid infrared (MIR) spectroscopy. A total of 145 soybean meal samples from different batches were collected and urea polymer was added into pure soybean meal with the concentration from 0.08% to 5.00%. The classification models for adulteration of soybean meal were constructed by partial least square discriminant analysis (PLS-DA) and support vector machine (SVM) using NIR spectroscopy and MIR spectroscopy data. The results showed that the SVM classification accuracy was 99.8% in training set and 99.2% in testing set with the limit of detection (LOD) 1.0% after standardization of variables in NIR spectrum. The SVM and PLS-DA classification accuracies were 100.0% with 0.08% LOD in MIR spectrum. The results indicate that the both infrared (NIR and MIR) techniques can represent a reliable and rapid classification tool on adulterated soybean meal, and the sensitivity of MIR is higher than NIR.

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