Nondestructive Classification of Black Tea Grades Based on Hyperspectral Imaging and Machine Learning

Tea grade classification is a crucial standard in assessing tea quality, allowing for a rapid evaluation of the general quality of tea. This paper proposes a method for black tea grade classification based on hyperspectral imaging technology, chemometric algorithms, and machine learning. Standard Normal Variate (SNV) was used to preprocess the hyperspectral data. Then, correlation analysis was conducted between tea grades and chemical values to identify the chemical values that directly reflect tea grades. To reduce redundant wavelengths and improve model accuracy, Successive Projections Algorithm (SPA) and Competitive Adaptive Reweighted Sampling (CARS) were employed for secondary feature wavelength selection. Support Vector Machine (SVM) was used to establish classification models for single and multiple fused chemical values. The results show that combining hyperspectral imaging technology with machine learning can achieve excellent results in tea grade classification, with an accuracy of $\mathbf{8 8 \%}$. This method provides a theoretical basis for the rapid and non-destructive online classification of black tea grades.