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

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, Naïve 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. Novelty Impact Statement Traditional methods for the classification of tea varieties mainly focus on the appearance of tea and depend on human sensory evaluation, which is expensive and time-consuming. In this study, a method involving fluorescence hyperspectral image technology with the CARS-ABC-SVM algorithm successfully was used for precise and non-destructive classification of tea varieties.