Evaluation of the Black Tea Taste Quality during Fermentation Process Using Image and Spectral Fusion Features

Abstract

The rapid and intelligent evaluation of black tea taste quality during the fermentation process is an unsolved problem because of the complexity and hysteretic of the current taste evaluation method. Common infrared spectroscopy and machine vision technologies can rapidly evaluate the taste quality of black tea, but they can not obtain comprehensive sample information. To obtain comprehensive sample information and achieve the rapid evaluation of the taste quality of black tea, the fusion data from hyperspectral images of fermentation samples were applied to predict the taste quality. The successive projection algorithm (SPA) and ant colony optimization (ACO) were used to select effective bands for spectral data. Subsequently, the color images were synthesized using three carefully selected effective bands obtained through the SPA and ACO. The 18 image features were extracted from each synthesized color image and fused with spectral effective bands. The fusion data and three different algorithms, such as partial least squares regression (PLSR), support vector machine regression (SVR), and extreme learning machine (ELM), were employed to establish the regression model for taste quality. Specifically, the fusion-SPA-PLSR model exhibited the best performance. This study provides a novel method for the intelligent evaluation of taste quality during black tea fermentation and lays a theoretical foundation for the intelligent processing and control of black tea.