基于拉曼光谱的大米快速分类判别方法

以拉曼光谱技术为手段,结合化学计量学方法,对来自黑龙江、江苏、湖南3个产地共123份大米样品的光谱数据进行采集,并对得到的拉曼图谱进行主成分分析(PCA)和偏最小二乘判别分析(PLSDA),建立大米快速分类判别方法。应用主成分分析对不同种类、产地和品种的大米进行粗分类鉴别;选择不同种类、品种和产地的稻米样本建立相应的偏最小二乘判别分析模型,其中2/3的样本作为建模训练集,1/3的样本作为建模校正集,按照种类、产地、品种建立的模型其训练集样本正确判别率均为100%,校正集样本正确判别率分别为100%,100%,94.12%。因此,研究所建立的拉曼光谱技术结合化学计量学方法可以快速、有效地鉴别大米种类、品种及产地。

Method for rapid discrimination of varieties rice by using Raman spectroscopy

The aim of this research was to establish the fast detection method for distinguishing the rice samples from different geographical origins and different breeds in China by using Raman micro-spectroscopy. A total of 123 rice samples from Heilongjiang, Jiangsu and Hunan province were analyzed by Raman spectra, and the data were statistic analyzed by chemometrics. The data was subjected to principal component analysis (PCA) and partial least squares discriminant analysis (PLSDA) to distinguish differences among samples from different geographical origins and different species. PCA could classify the rice samples preliminarily and classification model developed by PLSDA was used to classify and predict different rice samples. The rice samples (2/3) were as a training set of modeling, and the rest of samples were as a prediction set of modeling. The correct classification rates in the training set according to the varieties of rice samples were 100%, and those prediction set were 100%, 100% and 94.12%, respectively. The results in this research indicated it is a quickly efficacious method to identify rice from different geographical origins and species by Raman spectroscopy with chemometrics.