Abstract

Application of machine learning (ML)-based algorithms in food drying is an exciting and innovative approach to advance the drying technology. In order to appropriately develop this novel approach in all aspects of food drying field, significant scientific research is required. The main aspects of food drying research are the determination of material properties, microstructural characterization, mathematical modeling, and process optimization. It is essential to express this fundamental information through ML-based algorithms to advance the food drying research. This paper aims to present a comprehensive review of the application of machine learning-based approaches in food drying modeling, property prediction, microstructural characterization, and process parameters optimization. Moreover, this paper discusses the possibilities and challenges to apply ML-based algorithms in multiscale modeling and microwave-based hybrid drying. It is expected that this review paper will be beneficial in advancing the machine learning-based food drying technology.

摘要

基于机器学习（ML）的算法在食品干燥中的应用是促进干燥技术发展的令人兴奋且创新的方法。为了在食品干燥领域的各个方面适当地开发这种新颖的方法，需要大量的科学研究。食品干燥研究的主要方面是材料特性的确定，微观结构表征，数学建模和过程优化。必须通过基于ML的算法来表达这些基本信息，以推进食品干燥研究。本文旨在对基于机器学习的方法在食品干燥建模，属性预测，微观结构表征和过程参数优化中的应用进行全面综述。此外，本文讨论了将基于ML的算法应用于多尺度建模和基于微波的混合干燥的可能性和挑战。希望这篇评论文章对推进基于机器学习的食品干燥技术将是有益的。