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Artificial neural network-based shelf life prediction approach in the food storage process: A review

文献类型： 外文期刊

作者： Shi, Ce 1 ; Zhao, Zhiyao 5 ; Jia, Zhixin 1 ; Hou, Mengyuan 1 ; Yang, Xinting 1 ; Ying, Xiaoguo 6 ; Ji, Zengtao 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing, Peoples R China

2.Natl Engn Res Ctr Informat Technol Agr, Beijing, Peoples R China

3.Minist Agr & Rural Affairs, Key Lab Cold Chain Logist Technol Agroprod, Beijing, Peoples R China

4.Natl Engn Lab Agriprod Qual Traceabil, Beijing, Peoples R China

5.Beijing Technol & Business Univ, Beijing, Peoples R China

6.Zhejiang Ocean Univ, Coll Food & Pharm, Collaborat Innovat Ctr Seafood Deep Proc, Zhejiang Prov Key Lab Hlth Risk Factors Seafood, Zhoushan, Peoples R China

关键词： Artificial neural network; modeling; shelf life prediction; food; storage

期刊名称：CRITICAL REVIEWS IN FOOD SCIENCE AND NUTRITION （ 影响因子：10.2； 五年影响因子：11.8 ）

ISSN： 1040-8398

年卷期： 2023 年

页码：

收录情况： SCI

摘要： The prediction of food shelf life has become a vital tool for distributors and consumers, enabling them to determine storage and optimal edible time, thus avoiding unexpected food waste. Artificial neural network (ANN) have emerged as an effective, fast and accurate method for modeling, simulating and predicting shelf life in food. ANNs are capable of tackling nonlinear, complex and ill-defined problems between the variables without prior knowledge. ANN model exhibited excellent fit performance evidenced by low root mean squared error and high correlation coefficient. The low relative error between actual values and predicted values from the ANN model demonstrates its high accuracy. This paper describes the modeling of ANN in food quality prediction, encompassing commonly used ANN architectures, ANN simulation techniques, and criteria for evaluating ANN model performance. The review focuses on the application of ANN for modeling nonlinear food quality during storage, including dairy, meat, aquatic, fruits, and vegetables products. The future prospects of ANN development mainly focus on optimal models and learning algorithm selection, multiple model fusion, self-learning and self-correcting shelf-life prediction model development, and the potential utilization of deep learning techniques. Highlights ANN-based food shelf life prediction methods are reviewed. This paper discusses application of ANN in the food storage process. BPNN is the mainstream ANN architecture used for the prediction of food quality. ANNs are useful for prediction of outputs with high accuracy. Future trends of ANN in the agri-supply chain are evaluated.

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