外文摘要：Consumers have been more inclined towards functional food due to various health issues. Non-dairy-based probiotic milk could be the potential alternative to fulfill consumer demand. Spray drying of functional health drinks could enhance the shelf life and reduce the transportation cost of developed food powder. The current study was focused on the modeling and optimization of spray-drying process parameters using an artificial neural network (ANN) coupled with a genetic algorithm (GA). The data so obtained using ANN-GA was also compared with the data obtained using response surface methodology (RSM) coupled with desirability function (DF). The results indicated that the ANN model was better at predicting the response parameters compared to the RSM model with a higher correlation coefficient (R) of .9997, .9994, .9964, and .9992 for training, testing, validation, and all datasets, respectively. The optimum conditions obtained using RSM-DF were 160.41 degrees C of inlet air temperature, 33.77% of maltodextrin content, and 138.79 mL/h of feed rate while that for ANN-GA were 160.87 degrees C, 20%, and 200 mL/h. The RSM-DF method proved to be better for the optimization of response parameters. Therefore method selection for modeling and optimization of process and response parameters must be based on fulfilling the specific criteria.
外文关键词：artificial neural network;genetic algorithm;modeling and optimization;probiotic powder;spray-drying
作者：Mishra, Sabyasachi;Yadav, Shweta
作者单位：Natl Inst Technol;Natl Inst Food Technol Entrepreneurship & Manageme
期刊名称：JOURNAL OF FOOD PROCESS ENGINEERING
期刊影响因子：0.0
出版年份：2024
出版刊次：47（1）
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台