Department of Food Science, University of Tennessee, Knoxville, TN, USA.
J Sci Food Agric. 2022 Nov;102(14):6202-6210. doi: 10.1002/jsfa.12008. Epub 2022 May 28.
The microwave-assisted thermal process is a high-efficiency drying method and is promising to be applied in the food industry. However, the prediction of the thermal treatment results from such a dynamic and complicated process can be difficult. Additionally, the determination of the optimal drying parameters, such as drying temperature, microwave power, and drying time for optimized performance can also be hard. Recently, extensive research has been focusing on the use of artificial neural network (ANN) models in the laboratory-scale microwave drying processes and has shown the feasibility of such application. As a regression tool, the ANN models have been widely used in predicting drying performance; when integrated with additional optimizing algorithms, the ANN models could be used for drying parameter optimization; and when combined with real-time measuring techniques (e.g. nuclear magnetic resonance), the ANN models could be used for monitoring and controlling the drying process in a dynamic sense. Future research could focus on testing the developed ANN models in industrial-scale microwave drying processes and applying the ANN models in microwave drying kinetics research for optimizing the dynamic drying processes. © 2022 Society of Chemical Industry.
微波辅助热过程是一种高效的干燥方法，有望在食品工业中得到应用。然而，对于如此动态和复杂的过程，预测其热处理结果可能具有挑战性。此外，确定最佳干燥参数（如干燥温度、微波功率和干燥时间）以实现最佳性能也可能具有挑战性。最近，广泛的研究集中在人工神经网络（ANN）模型在实验室规模的微波干燥过程中的应用上，并显示了这种应用的可行性。作为一种回归工具，ANN 模型已广泛用于预测干燥性能；当与附加优化算法集成时，ANN 模型可用于干燥参数优化；当与实时测量技术（如核磁共振）结合使用时，ANN 模型可用于动态监测和控制干燥过程。未来的研究可以集中在测试开发的 ANN 模型在工业规模的微波干燥过程中的应用，并将 ANN 模型应用于微波干燥动力学研究，以优化动态干燥过程。© 2022 化学工业学会。
