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红外光谱对不同品种及产地小米的鉴别
引用本文:田雪,车前,严伟敏,欧全宏,时有明,刘刚.红外光谱对不同品种及产地小米的鉴别[J].光谱学与光谱分析,2022,42(6):1841-1847.
作者姓名:田雪  车前  严伟敏  欧全宏  时有明  刘刚
作者单位:1. 云南师范大学物理与电子信息学院,云南 昆明 650500
2. 曲靖师范学院物理与电子工程学院,云南 曲靖 655011
基金项目:国家自然科学基金项目(31760341);
摘    要:不同品种及产地的小米在口感及营养价值上存在显著差异,因此区分不同种小米对消费者具有参考意义。将傅里叶变换红外光谱(FTIR)、二维相关红外光谱(2D-IR)与曲线拟合、主成分分析(PCA)相结合,鉴别小米的品种及产地。结果显示:小米主要由碳水化合物、蛋白质和脂质组成,因此,其FTIR特征相似;二阶导数光谱(SD-IR)在3 012,2 962,2 928,2 856,1 748和1 548 cm-1附近的吸收峰强度存在明显差异;2D-IR在1 200~860和1 700~1 180 cm-1范围内,小米样品的自动峰和交叉峰数目、位置和强度差异明显;曲线拟合结果显示小米在1 700~1 600 cm-1范围内子峰面积比例不同,说明不同品种间小米的蛋白质含量不同,可以实现小米品种的鉴别分类;选取1 800~800 cm-1范围内的导数光谱进行主成分分析,前3个主成分累积贡献率为97%,不同产地的小米都得到正确归类。研究表明,红外光谱结合统计分析方法,是鉴别小米品种及产地的有效方法。

关 键 词:小米  红外光谱  曲线拟合  主成分分析  鉴别  
收稿时间:2021-05-12

Discrimination of Millet Varieties and Producing Areas Based on Infrared Spectroscopy
TIAN Xue,CHE Qian,YAN Wei-min,OU Quan-hong,SHI You-ming,LIU Gang.Discrimination of Millet Varieties and Producing Areas Based on Infrared Spectroscopy[J].Spectroscopy and Spectral Analysis,2022,42(6):1841-1847.
Authors:TIAN Xue  CHE Qian  YAN Wei-min  OU Quan-hong  SHI You-ming  LIU Gang
Institution:1. School of Physics and Electronic Information, Yunnan Normal University, Kunming 650500, China 2. School of Physics and Electronic Engineering, Qujing Normal University, Qujing 655011, China
Abstract:There are significant differences in taste and nutritional value among different varieties and producing areas of millet. Therefore, it is of reference significance for consumers to distinguish different kinds of millet. In this paper, Fourier transforms infrared (FT-IR) spectroscopy, two-dimensional correlation infrared (2D-IR) spectroscopy combined with curve fitting, principal component analysis (PCA) was used to distinguish varieties and origins of millet. The results showed that the original spectra of millet were similar, which were mainly composed of carbohydrates, proteins and lipids. The obvious differences in intensity were observed near 3 012, 2 962, 2 928, 2 856, 1 748 and 1 548 cm-1 in SD-IR. The numbers, positions and intensities of auto-peaks and cross-peaks were different in the range of 1200~860 and 1700~1180 cm-1. The curve fitting results showed that the ratio of the sub-peak areas of millet in the range of 1 700~1 600 cm-1 was different, which indicated that the protein content of millet was different among different varieties, to realize the identification of millet varieties. The range of 1 800~800 cm-1 in the derivative spectra was used for PCA analysis. The results showed that the cumulative contribution rate of the first three principal components was 97%, and millet from different producing areas was correctly classified. The study demonstrates that IR combined with statistical analysis methods could be effectively used to identify and analyze varieties and producing areas of millet.
Keywords:Millet  Infrared spectroscopy  Curve fitting  Principal component analysis  Discrimination  
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