Yang Ran, Wang Zhenbo, Chen Jiajia
Department of Food Science, University of Tennessee, Knoxville, TN 37996, USA.
Department of Mechanical, Aerospace and Biomedical Engineering, University of Tennessee, Knoxville, TN 37996, USA.
Foods. 2021 Apr 3;10(4):763. doi: 10.3390/foods10040763.
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.
机理建模一直是帮助食品科学家理解复杂的微波与食品相互作用的有用工具，但由于其资源密集型的特点，食品开发者无法直接将其用于食品设计。本研究开发并验证了一种将机理建模与机器学习相结合的综合方法，以实现高效的食品设计（厚度优化）并具有更好的加热均匀性。之前已广泛开发并验证了结合电磁学和传热的机理建模，并在本研究中直接使用。开发了一种贝叶斯优化机器学习算法并将其与机理建模相结合。通过将优化性能与仅基于机理建模的参数扫描方法进行比较，验证了该综合方法。结果表明，与参数扫描方法相比，该综合方法有能力且稳健地使用不同的初始训练数据集来优化不同形状产品的厚度，效率更高（提高了45.9%至62.1%）。打印了三个具有一种优化厚度（1.56厘米）和两种非优化厚度（1.20和2.00厘米）的矩形托盘，并用于微波加热实验，这证实了该综合方法在厚度优化方面的可行性。该综合方法可进一步开发和扩展为一个平台，以通过多参数优化有效地设计复杂的可微波食品。
