Rice origin identification can provide brand protection for rice with geographical indications. Therefore, it is necessary to design a convenient and fast detection method to meet the demands of the market. Based on Raman spectroscopy, this study involved the extraction and classification of characteristic spectral peaks of rice grain samples of the same cultivar but different places of origin. Raman spectra were acquired from 80 samples of rice with four different places of origin (Longjing 31 cultivar), and spectral information was extracted for analysis. First, the rice spectra were pretreated using a baseline correction and range normalization. Second, from the 400−1600cm⁻¹ and 2800−3200cm^-1 spectral region starting from the first four principal components of the regression coefficients extracted from principal component analysis (PCA), further screening produced eight spectral peaks characteristic of the place of origin: 476 cm⁻¹, 867 cm⁻¹,940 cm⁻¹, 1121 cm⁻¹, 1342 cm⁻¹, 1384 cm⁻¹, 1462 cm⁻¹, and 2914 cm⁻¹. These were also assigned to functional groups, revealing subtle differences in the nutritional content dependent on place of origin. Third, eight characteristic values extracted by PCA were used to establish a four-layer 8-9-6-4 (input-hidden-hidden-output) back propagation (BP) neural network structure as a rice-origin identification model. Finally, the model was used to train 80 rice samples in place-of-origin classification, and the average prediction accuracy of the cyclic test for the training samples reached 97.5 % after five epochs; for the other four epochs the accuracy ranged from 98.75%–96.25%. These results show that the model is feasible as a tool for the identification of rice types of the same variety and that it can effectively identify rice from different areas.

稻米原产地识别可以为具有地理标志的稻米提供品牌保护。因此，有必要设计一种方便快捷的检测方法来满足市场的需求。基于拉曼光谱法，这项研究涉及相同品种但产地不同的水稻籽粒样品特征光谱峰的提取和分类。从具有四个不同产地（龙井31品种）的80个水稻样品中获取拉曼光谱，并提取光谱信息进行分析。首先，使用基线校正和范围归一化对稻米光谱进行预处理。其次，从400-1600cm ^-1和2800-3200cm ^-1光谱区从主成分分析（PCA）中提取的回归系数的第一4种主成分起始，进一步筛选产生的产地的特性8个谱峰：476厘米^-1，867厘米^-1，940厘米^-1， 1121厘米^-1，1342厘米^-1，1384厘米^-1，1462厘米^-1，和2914厘米^-1。这些也被分配给功能组，揭示了营养成分之间细微的差异，取决于原产地。第三，利用PCA提取的八个特征值建立一个四层8-9-6-4（输入-隐藏-隐藏-输出）反向传播（BP）神经网络结构，作为水稻起源识别模型。最后，利用该模型对原产地分类的80个水稻样品进行了训练，经过5个历时周期，训练样品的循环测试平均预测准确率达到了97.5％。对于其他四个时期，精度范围为98.75％–96.25％。这些结果表明，该模型作为鉴定相同品种水稻类型的工具是可行的，并且可以有效地鉴定不同地区的水稻。