Classification of tea varieties based on fluorescence hyperspectral image technology and ABC-SVM algorithm

摘要

In this study, a rapid and non-destructive method for the classification of tea varieties based on fluorescence hyperspectral imaging technology was proposed in the wavelength range of 400.6797-1001.612 nm. Multiplication Scatter Correction (MSC) was used to preprocess the spectral data of tea samples. For optimal feature selection, variable iterative space shrinkage approach (VISSA) and competitive adaptive reweighed sampling (CARS) were established and CARS achieved good results on tea spectral data. Four linear and non-linear classification models, Naieve Bayes (NB), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Artificial Bee Colony Support Vector Machine (ABC-SVM) were established and then performances of classification models were compared according to classification accuracy. The classification accuracy of the ABC-SVM model coupled with CARS was achieved 100% which was the highest classification accuracy. The results of this study demonstrated that fluorescence hyperspectral image technology combined with the CARS-ABC-SVM model is feasible to classify tea varieties.