Research on Optimization of Food Process Parameters Based on Machine Learning

The article is limited to machine learning and discusses parameter optimisation in the bread production process. This paper introduces the basic technical parameters used in bread manufacturing: the moisture ratio, yeast fermentation time, kneading rate and baking temperature, as well as baking time, and how these parameters affect bread quality. The research collected plenty of process parameters and quality index through experiments, and established a prediction model of regression analysis, support vector machine and random forest based on machine learning theory. Meanwhile, the parameters were modified with genetic optimization and particle swarm optimization. It is possible to accurately analyze the relationship between the different process parameters and the quality of bread, optimize the production process, and upgrade the product quality, which has applicational value to the food production.