食品与发酵工业 >

2022 , Vol. 48 >Issue 12: 275 - 280

DOI: https://doi.org/10.13995/j.cnki.11-1802/ts.028608

分析与检测

基于近红外光谱结合化学计量学的转基因大豆产地判别

  • 雷渊雄 ,
  • 夏阿林 ,
  • 黄炜 ,
  • 侯泰东 ,
  • 王宏
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  • 1(邵阳学院 食品与化学工程学院,湖南 邵阳,422000)
    2(苏州观至光电科技有限公司,江苏 苏州,215000)
第一作者:硕士研究生(夏阿林副教授为通信作者,E-mail:alinxia@126.com)

收稿日期: 2021-07-09

  修回日期: 2021-07-29

  网络出版日期: 2022-07-15

基金资助

湖南省教育厅科学研究重点项目(16A236);邵阳学院研究生科研创新项目(CX2020SY042)

收起

Origin discrimination of transgenic soybean based on near infrared spectroscopy and chemometrics

  • LEI Yuanxiong ,
  • XIA Alin ,
  • HUANG Wei ,
  • HOU Taidong ,
  • WANG Hong
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  • 1(College of Food Science and Chemical Engineering, Shaoyang University, Shaoyang 422000, China)
    2(Suzhou Leaoptics Technology Co.Ltd., Suzhou 215000, China)

Received date: 2021-07-09

  Revised date: 2021-07-29

  Online published: 2022-07-15

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摘要

为探求无损、快速和准确判别转基因大豆产地的方法,该研究选取阿根廷转基因大豆、巴西转基因大豆、美国转基因大豆和加拿大转基因大豆样品共260份,将近红外光谱结合化学计量学对4种转基因大豆进行判别分析。利用近红外光谱仪采集260份样品的原始光谱,采用平滑+标准正态变量变换(standard normal variate transformation,SNV)方法对近红外光谱预处理。选取阿根廷转基因大豆、巴西转基因大豆、美国转基因大豆和加拿大转基因大豆样品共240份参与建模,Kennard-Stone(KS)算法划分训练集和预测集,主成分分析法(principal component analysis,PCA)、偏最小二乘判别分析(partial least squares-discriminate analysis,PLS-DA)和误差反向传播人工神经网络(back-propagation artificial neural network,BP-ANN)对预处理后的光谱数据进行分析。试验结果表明平滑+SNV的预处理方法能有效减少近红外光谱的噪音;PCA方法能判别出4种转基因大豆中的3种,阿根廷转基因大豆和加拿大转基因大豆不能同时判别;PLS-DA方法对训练集转基因大豆的判别正确率为93.9%,预测集判别正确率为88.3%;BP-ANN方法能够准确的判别4种转基因大豆,判别正确率为100%。用未参与建模的4种转基因大豆作为验证集对PLS-DA方法模型和BP-ANN方法模型进行验证,验证集中PLS-DA方法模型判别正确率为90.0%,BP-ANN方法模型判别正确率为100%。因此近红外光谱结合化学计量学可为转基因大豆的朔源提供较好的技术支持。

关键词： 近红外光谱; 化学计量学; 转基因大豆; 朔源

本文引用格式

雷渊雄 , 夏阿林 , 黄炜 , 侯泰东 , 王宏 . 基于近红外光谱结合化学计量学的转基因大豆产地判别[J]. 食品与发酵工业, 2022 , 48(12) : 275 -280 . DOI: 10.13995/j.cnki.11-1802/ts.028608

Abstract

In order to develop a nondestructive, rapid and accurate method to identify the origin of transgenic soybeans, 260 samples of transgenic soybeans from four different countries were selected in this study, and these four kinds of transgenic soybeans were analyzed by NIR spectroscopy combined with chemometrics. The original spectra of 260 samples were collected by NIR spectrometer, and the NIR spectra were preprocessed by smoothing +standard normal variate transformation (SNV) method. 240 samples of transgenic soybeans from Argentina, Brazil, America, Canada and Uruguay were selected for modeling. The Kennard-Stone (KS) algorithm divided the training set and the prediction set, principal component analysis (PCA), partial least squares-discriminate analysis (PLS-DA) and back-propagation artificial neural network (BP-ANN) were used to analyze the preprocessed spectral data. The experimental results showed that the preprocessing method of smoothing +SNV can effectively reduce the noise in the NIR spectrum. The PCA method could identify three of the four transgenic soybeans, while Argentina transgenic soybeans and Canada transgenic soybeans could not be identified simultaneously. The accuracy of PLS-DA method in identifying transgenic soybeans in training set was 93.9%, and that of the prediction set was 88.3%. The BP-ANN method was able to identify four kinds of transgenic soybeans accurately with the discrimination accuracy of 100%. The PLS-DA method model and BP-ANN method model were verified with four kinds of transgenic soybeans not involved in the modeling as the verification set. The discrimination accuracy of PLS-DA method model was 90.0%, and that of BP-ANN method model was 100%. Therefore, the combination of NIR spectroscopy and chemometrics could provide a good technical support for the origin discrimination of transgenic soybeans.

Key words： near infrared spectroscopy; stoichiometry; transgenic soybean; traceability

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