Fungal fermentation of Fuzhuan brick tea: A comprehensive evaluation of sensory properties using chemometrics, visible near-infrared spectroscopy, and electronic nose

Abstract

Fuzhuan brick tea (FBT) fungal fermentation is a key factor in achieving its unique dark color, aroma, and taste. Therefore, it is essential to develop a rapid and reliable method that could assess its quality during FBT fermentation process. This study focused on using electronic nose (e-nose) and spectroscopy combination with sensory evaluations and physicochemical measurements for building machine learning (ML) models of FBT. The results showed that the fused data achieved 100 % accuracy in classifying the FBT fermentation process. The SPA-MLR method was the best prediction model for FBT quality (R² = 0.95, RMSEP = 0.07, RPD = 4.23), and the fermentation process was visualized. Where, it was effectively detecting the degree of fermentation relationship with the quality characteristics. In conclusion, the current study's novelty comes from the established real-time method that could sensitively detect the unique post-fermentation quality components based on the integration of spectral, and e-nose and ML approaches.

Keywords: Data fusion; Fermented dark tea; Multiple sensor technologies; Spectroscopy; Tea quality assessment.