文章基本信息

标题：An Integrated Approach of Mechanistic-Modeling and Machine-Learning for Thickness Optimization of Frozen Microwaveable Foods
本地全文：下载
作者：Ran Yang ; Zhenbo Wang ; Jiajia Chen 等
期刊名称：Foods
电子版ISSN：2304-8158
出版年度：2021
卷号：10
期号：4
页码：763
DOI：10.3390/foods10040763
出版社：MDPI Publishing
摘要：Mechanistic-modeling has been a useful tool to help food scientists in understanding complicated microwave-food interactions, but it cannot be directly used by the food developers for food design due to its resource-intensive characteristic. This study developed and validated an integrated approach that coupled mechanistic-modeling and machine-learning to achieve efficient food product design (thickness optimization) with better heating uniformity. The mechanistic-modeling that incorporated electromagnetics and heat transfer was previously developed and validated extensively and was used directly in this study. A Bayesian optimization machine-learning algorithm was developed and integrated with the mechanistic-modeling. The integrated approach was validated by comparing the optimization performance with a parametric sweep approach, which is solely based on mechanistic-modeling. The results showed that the integrated approach had the capability and robustness to optimize the thickness of different-shape products using different initial training datasets with higher efficiency (45.9% to 62.1% improvement) than the parametric sweep approach. Three rectangular-shape trays with one optimized thickness (1.56 cm) and two non-optimized thicknesses (1.20 and 2.00 cm) were 3-D printed and used in microwave heating experiments, which confirmed the feasibility of the integrated approach in thickness optimization. The integrated approach can be further developed and extended as a platform to efficiently design complicated microwavable foods with multiple-parameter optimization.
关键词：mechanistic-modeling; machine-learning; microwaveable food design; Bayesian optimization; thickness; heating uniformity mechanistic-modeling ; machine-learning ; microwaveable food design ; Bayesian optimization ; thickness ; heating uniformity