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

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 Wilks Λ statistics, which were then ranked in an increasing order. Then, the weight coefficient curves of the principal components corresponding to the first three smallest Wilks Λ values 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 Wilks Λ statistics, 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.