留兰香高光谱特征波长稳健提取及产地鉴别模型构建

Robust Extraction of Hyperspectral Feature Wavelengths and Development of a Model for Geographical Origin Identification of Spearmint

本研究通过高光谱技术解决留兰香的产地鉴别问题。首先,选取了5个产地共375个留兰香样本进行高光谱数据采集,并采用多元散射校正方法进行预处理,对预处理后的数据进行主成分分析(principal component analysis,PCA),将得到的主成分变量构建WilksΛ统计量,且从小到大排序。然后,分别绘制前3小WilksΛ值所对应的主成分在各个波长下的权重系数曲线,系数曲线上的峰谷所对应的波长为特征波长,共得到37个特征波长。随后用Fisher判别分析构造鉴别模型的输入变量。最后,分别构建支持向量机(support vector machine,SVM)和反向传播神经网络(back propagation neural network,BPNN)产地鉴别模型。结果表明:SVM模型的鉴别效果优于BPNN模型,其训练集和测试集鉴别正确率分别为99.67%和98.67%。因此,在PCA联合WilksΛ统计量提取特征波长基础上构建的留兰香产地SVM鉴别模型可有效实现产地的鉴别,并且所提取的特征波长不受到理化指标数量的影响,使该鉴别模型具有较强的鲁棒性。

In this study,hyperspectral technology was used to solve the problem of identifying the geographical origin of spearmint.First,375 spearmint leaf samples from 5 geographical origins were selected for hyperspectral data collection,and the hyperspectral data were preprocessed by multiplicative scatter correction(MSC)and analyzed by principal component analysis(PCA);the principal component variables were used to construct WilksΛstatistics,which were then ranked in an increasing order.Then,the weight coefficient curves of the principal components corresponding to the first three smallest WilksΛvalues were drawn at each wavelength;a total of 37 feature wavelengths were obtained,namely the peak and valley wavelengths in the coefficient curves.Next,Fisher discriminant analysis(FDA)was used to construct the input variables for a model for graphical origin identification of spearmint.At last,a support vector machine(SVM)model and a back propagation neural network(BPNN)model for identifying the geographical origin of spearmint were constructed.The results indicated that the SVM model outperformed the BPNN model,with discrimination accuracy of 99.67%and 98.67%in the training and test sets,respectively.Therefore,the SVM model,constructed using PCA combined with WilksΛstatistics,can effectively identify the geographical origin of spearmint.In this model,the extracted feature wavelengths are not affected by the number of physicochemical indexes,making the model robustness.