基于近红外光谱技术的大米品种快速鉴别方法

为探索大米无损检测技术，提出了一种基于可见-近红外光谱技术快速、无损鉴别大米品种的新方法。首先采用主成分分析法对大米品种进行聚类，然后利用小波变换技术提取光谱特征信息，把光谱特征信息作为人工神经网络的输入建立品种识别模型，对大米品种进行鉴别。从每种大米60个样本共计180个样本中随机抽取150个样本（每种50个样本）用来建立神经网络模型，剩下的30个大米样本用于预测。品种识别准确率达到100%。说明所提出的方法具有很好的分类和鉴别作用，为大米的品种鉴别提供了一种新方法。

Method for rapid discrimination of varieties of rice using visible NIR spectroscopy

Based on the visible-near infrared spectroscopy (Vis-NIRS) technology, a new method to discriminate varieties of rice was proposed. First, the clustering of varieties of rice was analyzed by principal component analysis (PCA). Second, characteristics information of spectra were extracted by wavelet transform (WT), which as input sets for artificial neural network (ANN) to discriminate rice varieties of rice. And then a total of 180 (60 in each category) samples of three categories were adopted in this study, with 150 (50 in each category) for training sets and the remaining 30 (10 for each category) for prediction sets. The experimental results show that the identification rate reached 99.3%, which proves that the new method proposed in this study is capable to discriminate the varieties of rice with high accuracy. In addition, it might provide a new method to discriminate rice varieties.