基于近红外光谱技术的小米产地溯源研究

采用便携式近红外光谱仪结合主成分分析(PCA)、费舍尔线性判别(FLDA)及多层感知器神经网络(MLPNN)模型,探讨近红外光谱技术应用于小米产地溯源的可行性。PCA分析显示,除山西、河南、黑龙江3省的样品差异较小难以区分外,其余8个省份的样品均能清晰区分产地。FLDA和MLP-NN分析均能识别出小米样品产地,但MLP-NN识别效果优于FLDA,两个模型对预测集的识别正确率分别为92.3%,84.6%。以上结果表明,近红外光谱技术可有效应用于小米的产地溯源。

Geographic origin determination of millet based on near infrared spectroscopy technique

In order to provide a scientific method for the geographic origin determination of millet, a portable near-infrared (NIR) spectrometer was used in combination with other methods, including principal component analysis (PCA), linear discriminant analysis (LDA) and multi-layer perceptron neural network (MLP-NN). The results showed that, for PCA model, samples from different places except Shanxi, Henan and Heilongjiang provinces clustered into different groups significantly. Millet samples from different origins could be effectively discriminated by FLDA and MLP-NN. The MLP-NN model was better than FLDA model in recognition rate. The recognition accuracy of the two models for the predication set were 92.3% and 84.6% respectively. Therefore, the NIR spectroscopy technique can be used for the geographic origin determination of the millet.