Rapid evaluation of Pixian Douban meju in the tank fermentor Based on the image features and multi-model analysis

Abstract

Pixian Douban (PXDB) meju is a crucial intermediate product in the PXDB production. In this study, a machine vision system was employed to monitor and evaluate the meju quality quickly to replace the time-consuming chemical methods. The results of correlation analysis indicated that the physicochemical indicators were highly related to the color changes. The algorithmic results showed that the support vector machine was the most effective qualitative analysis method with 100% classification accuracy in the training set and 96.97% in the test set. The partial least square regression (PLSR) model showed high accuracy for the quantitative prediction of the meju, especially for the residual prediction deviation values of amino acid nitrogen and total titratable acid with 4.94 and 5.13, respectively. The distributions of physicochemical indicator contents at different fermentation stages were visualized by the PLSR model. This study provided a basis for monitoring the PXDB production in real time.