Lychee Variety Discrimination by Hyperspectral Imaging Coupled with Multivariate Classification

Abstract

This current study was carried out to investigate the ability of hyperspectral imaging (HSI) technique and multivariate classification for the differentiation of lychee varieties. A total of 122 lychee samples from three varieties (“Baila,” “Jizhui,” and “Guiwei”) were used. The relationship between reflectance spectra and lychee varieties were established. Principal component analysis (PCA) was implemented on the region of interest (ROI) image to reduce data dimensionality and visualize the cluster trend. The first two principal components (PCs) explained over 97 % of variances of all spectral bands. Linear (soft independent modeling of class analogy (SIMCA) and partial least square discriminant analysis (PLS-DA)) and nonlinear (back propagation neural network (BPNN) and support vector machine (SVM)) multivariate classification methods were used to develop discrimination models. The results revealed that SVM model achieved the best result, with the identification rate of 100 % in the calibration set and 87.81 % in the prediction set. BPNN had a discrimination rate of 100 % for the training set and 85.37 % for prediction set, while PSL-DA and SIMCA model had a discrimination rate of 78.05 % and 60.98 % for prediction sets, respectively. The nonlinear classification methods used were superior to the linear ones. The overall results showed that HSI system with SVM classification tool could be used in identification of lychee varieties.