Level: Intermediate
【Presenter】
PATRICK GIULIANO, Senior Quality Engineer, Abbott
Charles Chen, Continuous Improvement Expert, Statistics-Aided Engineering (SAE), Applied Materials
Mason Chen, High School Student, Stanford Online High School
[Presentation summary] (using automatic translation)
Ready-to-eat foods, such as dumplings, are typically prepared without precise process control of cooking parameters. The purpose of this work is to customize the dumpling cooking process based on different dumpling product types. In the gyoza cooking process, water temperature and cooking time are the most important factors that determine the degree of cooking (doneness) of the gyoza. Dumpling weight, dumpling type, and batch size are also variables that affect the cooking process. We built a structured JMPDSD platform with special properties to build a cooking time prediction model. The internationally recognized ISO22000 Food Safety Management and Hazard Analysis Critical Control Points (HACCP) schema has been adopted. We compared the JMP neural fitting method using modern data mining algorithms with RSM. The results showed a greater prevalence of main effects from factors such as boiling temperature, product type, dumpling size/batch, and interaction effects constrained by the mixture used in the dumpling composition. This design/analysis approach employed JMP's robust design optimization, Monte Carlo simulation, and HACCP control limits to understand and characterize the sensitivity of dumpling cooking elements to resulting cooking times. The holistic approach showed the synergy of combining models with different projective properties. Recursive partition-based AI models use classification schemas to estimate interaction effects, and classical (stepwise) regression modeling provides the ability to interpret interactions of two or more degrees, including likelihood. Curvature in quadratic terms. In this whitepaper, we have described a new automated dumpling cooking process and analysis framework (using AI schema) that can improve the entire process, reduce energy costs, and reduce labor costs. This new methodology has the potential to change the way we think about estimating business costs and modeling profits in the restaurant industry.
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