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本文利用傅里叶变换红外光谱(FTIR)结合主成分分析和聚类分析对白小米、黄小米、糯小米、青小米、陈黄小米、黑小米和大黄米进行鉴别研究。所有样品的傅里叶变换红外光谱整体相似,二阶导数光谱存在明显的差异。选取1800~1400cm-1范围内的二阶导数光谱数据对52份小米样品做多变量分析,结果显示,主成分分析的分类准确率为84.6%,系统聚类分析的分类准确率为92.3%。结果表明傅里叶变换红外光谱技术结合化学计量学能有效地区分不同品种的小米,为不同小米的分类鉴定提供新的方法与途径。
In this paper, the identification of white millet, yellow millet, glutinous millet, green millet, Chenhuang millet, black millet and rhubarb by Fourier transform infrared spectroscopy (FTIR) combined with principal component analysis and cluster analysis. The Fourier transform infrared spectra of all samples are similar overall, and there are obvious differences in the second derivative spectra. Multivariate analysis of 52 millet samples from the second derivative data in the range of 1800 ~ 1400cm-1 showed that the accuracy of classification of principal component analysis was 84.6% and that of systematic cluster analysis was 92.3%. The results show that Fourier transform infrared spectroscopy combined with chemometrics can effectively distinguish different varieties of millet, and provide a new method and method for the classification and identification of different millet varieties.