{"title":"Application of Artificial Neural Network (ANN) in Ultrasound-Assisted Extraction of Bioactive Compounds","authors":"Sourav Chakraborty, Maitreye Das, Tabli Ghosh, Kshirod Kumar Dash","doi":"10.1111/jfpe.70028","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>Artificial neural network (ANN) is regarded as a promising tool among the recent trends of modeling methodologies for the nonlinear mapping of multiple variables. In the case of the ultrasound-assisted extraction (UAE) process, the majority of the response-based predictive models are administered with nonlinear modeling approaches. Numerical models provide restricted extension and abilities to show such nonlinear prescient conditions. Due to this, ANN modeling gets considerable scope for effectively mapping the variables. In addition, the essential of no prior specific, in the event that there ought to emerge an event of fitting the data or information, make ANN the most popular and compelling one for modeling the extraction process. This paper provides the basics of principles and applications of ANNs in process modeling and optimization of different UAE-based extraction perspectives. In addition, the prevalence of the UAE process in diverging from other extraction methods is highlighted. Further, the impact of various process factors like ultrasound intensity, exposure time, and temperature during the UAE interaction and their prescient modeling approaches for efficient controlling of the responses are detailed. Hence, this review would be useful for modeling and optimization of thermal and non-thermal-based extraction processes, which would assist the food processing industries to improve food quality.</p>\n </div>","PeriodicalId":15932,"journal":{"name":"Journal of Food Process Engineering","volume":"48 1","pages":""},"PeriodicalIF":2.7000,"publicationDate":"2025-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Food Process Engineering","FirstCategoryId":"97","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/jfpe.70028","RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Artificial neural network (ANN) is regarded as a promising tool among the recent trends of modeling methodologies for the nonlinear mapping of multiple variables. In the case of the ultrasound-assisted extraction (UAE) process, the majority of the response-based predictive models are administered with nonlinear modeling approaches. Numerical models provide restricted extension and abilities to show such nonlinear prescient conditions. Due to this, ANN modeling gets considerable scope for effectively mapping the variables. In addition, the essential of no prior specific, in the event that there ought to emerge an event of fitting the data or information, make ANN the most popular and compelling one for modeling the extraction process. This paper provides the basics of principles and applications of ANNs in process modeling and optimization of different UAE-based extraction perspectives. In addition, the prevalence of the UAE process in diverging from other extraction methods is highlighted. Further, the impact of various process factors like ultrasound intensity, exposure time, and temperature during the UAE interaction and their prescient modeling approaches for efficient controlling of the responses are detailed. Hence, this review would be useful for modeling and optimization of thermal and non-thermal-based extraction processes, which would assist the food processing industries to improve food quality.
期刊介绍:
This international research journal focuses on the engineering aspects of post-production handling, storage, processing, packaging, and distribution of food. Read by researchers, food and chemical engineers, and industry experts, this is the only international journal specifically devoted to the engineering aspects of food processing. Co-Editors M. Elena Castell-Perez and Rosana Moreira, both of Texas A&M University, welcome papers covering the best original research on applications of engineering principles and concepts to food and food processes.