Use of data-driven model analysis to develop raw material quality standards for frozen dumplings

In order to enable companies to directly infer the quality standard of the requisite frozen dumpling ingredients based on the quality of the frozen dumplings, this paper proposes a data-driven model-based analysis method. The method firstly studies the core indicators and main components of glutinous rice flour through data analysis, and secondly uses the core indicators and main components of glutinous rice flour to predict the quality indicators of instant frozen dumplings. Then the upper and lower bounds of the range of variation of the core indicators of glutinous rice flour are used as decision variables, and the quality requirements of frozen soup dumplings and glutinous rice flour itself are used as constraints to find the region of the range of variation of the indicators that are dedicated to glutinous rice flour as widely as possible. Finally, we adjusted the model, and the optimal range of glutinous rice flour indexes included almost all raw materials with high sensory score in the sample data, and solved the problem of setting quality standards for special glutinous rice flour.