Accurately predict product temperature during high-temperature Daqu fermentation process based on mathematical modelling

In order to scientifically evaluate and predict the fermentation temperature change trend of Daqu, using the representative Daqu-making rounds (the first, third, fourth and fifth rounds) in spring, summer, autumn and winter as research materials, comprehensively tracked and collected the fermentation temperature changes of 1 300 pieces of Daqu in two representative fermentation workshops. The fermentation temperature prediction model for the temperature of Daqu products in the whole workshop was established by principal component analysis (PCA) and cluster analysis (CA). To further verify the reliability of the prediction model, the temperature data of eight monitoring areas in another Daqu fermentation workshop were randomly paired and fed into the model for further verification. The results showed that the total temperature error of the predicted temperature and actual temperature of the first turning stage was less than 1.2 ℃, and the total temperature error of the second turning stage was less than 1.1 ℃. The average accuracy of the prediction model of Daqu-making temperature change data in four seasons was more than 95%, and there was no significant difference with the actual model in the test. The prediction model provided a scientific method for effective temperature evaluation of the whole pile, and also provided a scientific basis for temperature control of Daqu fermentation and its intelligent development.