（英）基于近红外的移动窗口BP神经网络实现山东绿茶产地溯源

将近红外光谱分析技术用于对山东省代表性绿茶(崂山绿茶和日照绿茶)进行快速、无损伤产地溯源.对平滑处理、一阶微分和二阶微分等几种不同的光谱预处理方法进行了系统性对比和研究创新.提出移动窗口BP神经网络(MW-BP-ANN)算法用于选择特征光谱变量.实验发现,一阶微分和移动窗口-BP神经网络可以大幅提高支持向量机(SVM)分类模型的预测能力.经预处理后,分类模型的最优鉴别准确率可达98.33%.研究结果表明,该光谱变量选择方法对提高产地溯源模型的预测能力起到至关重要作用.

Origin identification of Shandong green tea by moving window back propagation artificial neural network based on near infrared spectroscopy

Near infrared (NIR) spectroscopy was used to identify the origins of two representative of Shandong green tea (Laoshan green tea and Rizhao green tea) rapidly and non-destructively. Several preprocessing methods of NIR, such as smoothing, first-and second-derivative were compared. Moving window back propagation artificial neural network (MW-BP-ANN) was used to select characteristic spectral variables. It was found that the first-derivative and MW-BP-ANN processing techniques improved the predictive abilities of the support vector machine (SVM) classification models. The best estimated identification accuracy can be improved to 98.33%, which demonstrates that the spectral variables selection method is significant for the predictive ability of origin identification models.