当前位置： SCI文献检索 > CRITICAL REVIEWS IN FOOD SCIENCE AND NUTRITION期刊下所有文献 > Comprehensive study on applications of artificial neural network in food process modeling.

Comprehensive study on applications of artificial neural network in food process modeling.

Abstract：

:Artificial neural network (ANN) is a simplified model of the biological nervous system consisting of nerve cells or neurons. The application of ANN to food process engineering is relatively novel. ANN had been employed in diverse applications like food safety and quality analyses, food image analysis, and modeling of various thermal and non-thermal food-processing operations. ANN has the ability to map nonlinear relationships without any prior knowledge and predicts responses even with incomplete information. Every neural network possesses data in the form of connection weights interconnecting lines between the input to hidden layer neurons and weights of hidden to output layer neurons, which has a significant role in predicting the output data. The applications of ANN in different unit operations in food processing were described that includes theoretical developments using intelligent characteristics for adaptability, automatic learning, classification, and prediction. The parallel architecture of ANN resulted in a fast response and low computational time making it suitable for application in real-time systems of different food process operations. The predicted responses obtained by the ANN model exhibited high accuracy due to lower relative deviation and root mean squared error and higher correlation coefficient. This paper presented the various applications of ANN for modeling nonlinear food engineering problems. The application of ANN in the modeling of the processes such as extraction, extrusion, drying, filtration, canning, fermentation, baking, dairy processing, and quality evaluation was reviewed. HIGHLIGHTS 1. This paper discusses application of ANN in different emerging trends in food process. 2. Application of ANN to develop non-linear multivariate modeling is illustrated. 3. ANNs have been shown to be useful tool for prediction of outcomes with high accuracy. 4. ANN resulted in fast response making it suitable for application in real time systems.

journal_name

Crit Rev Food Sci Nutr

journal_title

Critical reviews in food science and nutrition

authors

Bhagya Raj GVS,Dash KK

doi

10.1080/10408398.2020.1858398

subject

Has Abstract

pub_date

2020-12-17 00:00:00

pages

1-28

eissn

1040-8398

issn

1549-7852

pub_type

杂志文章

在线工具

Comprehensive study on applications of artificial neural network in food process modeling.