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.

Fermentation is a key link in determining the quality and flavor formation of black tea. However, during the actual production, the judgment of black tea fermentation quality mainly relies on the sensory evaluation of the tea maker, which is more subjective and prone to cause inconsistency in tea quality. Traditional testing methods, such as physical and chemical analyses, are time-consuming, laborious, and costly and are unable to meet the needs of the actual production. In this study, a self-developed machine vision system was used to quickly and accurately identify the degree of black tea fermentation by acquiring color and texture information on the surface of fermented leaves. To accurately control the quality of black tea fermentation and to understand the dynamic changes in key endoplasmic components in the fermented leaves, a quantitative prediction model of the key endoplasmic components in the fermentation process of black tea was constructed. The experiments proved that the system achieved 100% accuracy in discriminating the degree of fermentation of black tea, and the prediction accuracy of catechin components and thearubigin content reached more than 0.895. This system overcomes the defects of accurate measurement of multiple sensors coupled together, reduces the detection cost, and optimizes the experimental process. It can meet the needs of online monitoring in actual production.