Rapid monitoring of black tea fermentation quality based on a solution-phase sensor array combined with UV-visible spectroscopy

Rapid monitoring of fermentation quality has been the key to realizing the intelligent processing of black tea. In our study, mixing ratios, sensing array components and reaction times were optimized before an optimal solution phase colorimetric sensor array was constructed. The characteristic spectral information of the array was obtained by UV-visible spectroscopy and subsequently combined with machine learning algorithms to construct a black tea fermentation quality evaluation model. The competitive adaptive reweighting algorithms (CARS)-support vector machine model discriminated the black tea fermentation degree with 100% accuracy. For quantification of catechins and four theaflavins (TF, TFDG, TF-3-G, and TF-3'-G), the correlation coefficients of the CARS least square support vector machine model prediction set were 0.91, 0.86, 0.76, 0.72 and 0.79, respectively. The results obtained within 2 min enabled accurate monitoring of the fermentation quality of black tea, which provides a new method and idea for intelligent black tea processing.

Automated summary

In this study, a black tea fermentation quality evaluation model was constructed by optimizing mixing ratios, sensing array components, and reaction times, and utilizing the CARS-support vector machine model. The model achieved 100% accuracy in discriminating the fermentation degree of black tea and also showed good prediction results for catechins and different theaflavins. The results obtained within 2 minutes provide a new method and idea for intelligent processing of black tea.