基于拉曼光谱检测技术结合化学判别方法, 建立新陈大米拉曼光谱判别模型; 建立适当的样品预处理方法, 确保样品制备的均一性, 使用拉曼光谱仪对新陈大米共计60组样品进行检测, 在785 nm波长激光激发下, 获取样品200～2 400 cm-1的拉曼光谱信息; 对原始拉曼光谱进行基线校正、 平滑、 滤波等处理。 利用主成分分析法（PCA）对拉曼光谱进行降维处理及粗分类鉴别; 基于偏最小二乘分析法（PLS）, 建立新陈大米快速鉴别模型, 该模型对建模训练集鉴别正确率为100%, 模型验证集鉴别正确率为95%。 结果表明: 该模型判断新陈大米是可行的, 为大米新陈度的快速判别提供了一种新的方法。

A prediction discrimination model of fresh and stale rice was established based on Raman Spectroscopy and Chemometrics. The pretreatment processes have to be employed before the experiment. A total of 60 samples were put in the special box. The samples were measured by 785nm Raman spectrometer, which can collect spectral range of 200~2 400 cm-1. Smoothing, baseline correction were conducted to process the raw spectra. Principal Component Analysis (PCA) was employed to reduce dimension analysis of full-wave band of fresh and stale rice, and it could classify the samples preliminarily. The discrimination model was developed with Partal Least Squares (PLS). The correct classification rates in the training set and prediction set were 100% and 95%, respectively. The results in this research indicated it is a quickly useful method to discriminate between fresh and stale rice.