Abstract
On-line monitoring of key growth indicators is essential to regulate the kombucha fermentation process and improve utilization of summer-autumn tea. This study developed an on-line visible/near-infrared spectroscopy (V-NIR) detection system and method to monitor the concentration of soluble sugars, total acids, and bacteria during kombucha fermentation. The partial least squares method (PLS) was coupled with the sparrow search algorithm (SSA), whale optimization algorithm (WOA), and African vulture optimization algorithm (AVOA) to select characteristic variables, extract high-frequency variables, and establish calibration models. In general, the model performance of the high-frequency variable subset was better than that of the selected variable subset. The correlation coefficients of the optimal model of the six indexes in the two stages were 0.8692, 0.8513, 0.9392, 0.9746, 0.9570, and 0.9830, respectively. The developed monitoring system and modelling strategy can be applied to the real-time online determination of kombucha fermentation and further applied to the food industry.
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