Nondestructive Detection of Keemun Black Tea Grade Based on Hyperspectral Imaging Technique

Abstract

In order to evaluate the grade of tea products quickly and nondestructive, the near infrared (900~1700 nm) hyperspectral imaging technology was applied to classify six grades of black tea. Firstly, the hyperspectral data were visualized by linear and nonlinear dimension reduction methods. The visualization algorithms included the linear method of principal component analysis (PCA), multi-dimensional scaling (MDS), the nonlinear method of T-distributed stochastic embedding (T-SNE) and Sammon nonlinear mapping. Secondly, the classification model was established by using support vector machine (SVM) and extreme learning machine (ELM) to identify the different grades of Keemun black tea. Finally, SVM and ELM classification model were used to identify each pixel of hyperspectral image, and the prediction map was obtained. The results showed that T-SNE could divide the six grades of Keemun black tea into six different clusters, and the accuracy of SVM and ELM prediction set was 100% and 96.35%, respectively. The T-SNE visualization effect was the best, and the detection model of SVM could effectively identify the six grades of Keemun black tea. This paper provides an effective method for rapid and nondestructive testing of tea product grade, which had great significance for quality control, authenticity and adulteration detection of tea products.