Risk early warning of food safety using novel long short-term memory neural network integrating sum product based analytic hierarchy process

Geng, Zhiqiang; Lingling, Liang; Han, Yongming; Guangcan, Tao; Chu, Chong

doi:10.1108/bfj-04-2021-0367

Cited by 16 publications

(8 citation statements)

References 31 publications

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“…Geng et al proposed a neural network based on the deep radial basis function (RBF) to build a food risk warning model [ 20 ]. They then combined the LSTM network with the method of data fuzzy transformation to fuse the data of various food detection indexes to obtain the comprehensive risk value [ 21 ]. However, the two models have their defects.…”

Section: Related Workmentioning

confidence: 99%

An ensemble of AHP-EW and AE-RNN for food safety risk early warning

et al. 2023

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Food safety problems are becoming increasingly severe in modern society, and establishing an accurate food safety risk warning and analysis model is of positive significance in avoiding food safety accidents. We propose an algorithmic framework that integrates the analytic hierarchy process based on the entropy weight (AHP-EW) and the autoencoder-recurrent neural network (AE-RNN). Specifically, the AHP-EW method is first used to obtain the weight percentages of each detection index. The comprehensive risk value of the product samples is obtained by weighted summation with the detection data, which is used as the expected output of the AE-RNN network. The AE-RNN network is constructed to predict the comprehensive risk value of unknown products. The detailed risk analysis and control measures are taken based on the risk value. We applied this method to the detection data of a dairy product brand in China for example validation. Compared with the performance of 3 models of the back propagation algorithm (BP), the long short-term memory network (LSTM), and the LSTM based on the attention mechanism (LSTM-Attention), the AE-RNN model has a shorter convergence time, predicts data more accurately. The root mean square error (RMSE) of experimental data is only 0.0018, proving that the model is feasible in practice and helps improve the food safety supervision system in China to avoid food safety incidents.

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Section: Related Workmentioning

confidence: 99%

An ensemble of AHP-EW and AE-RNN for food safety risk early warning

et al. 2023

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“…ANN fully considers interactions between variables in the modeling process and can identify complex nonlinear relationships between variables. Therefore, using ANN for the risk assessment of cold-chain logistics has better accuracy [ 32 , 33 ]. Xu et al [ 34 ] developed enterprise projects as evaluation indicators and improved the standard BP algorithm by adding momentum items and adjusting the learning rate, hidden-layer design, error function, and transformation function to predict risks in cold-chain logistics.…”

Section: Application Of a Data-driven Model For Cold Food Chain Risk ...mentioning

confidence: 99%

Data-Driven Analysis of Risk-Assessment Methods for Cold Food Chains

Wang

Zhao

Wang

2023

Foods

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The problem of cold-chain food safety is becoming increasingly prominent. Cold food chain risk assessment is an important way to ensure cold-chain food safety. Using CiteSpace, this study analyzes the knowledge map of research hotspots in the field of cold-chain food safety over the past 18 years, identifies the research keywords, presents the centrality statistics, and calculates the cluster values and average cluster contour values. Adopting a data-driven perspective, risk-assessment methods for cold food chains are summarized based on qualitative risk assessment, quantitative risk assessment, and comprehensive qualitative and quantitative risk assessment. The advantages and disadvantages of each are summarized. Finally, the problems and challenges in current cold food chain risk-assessment research are summarized in three aspects: the data credibility of cold food chain traceability systems, cold-chain food safety audit methods, and nontraditional cold food chain risk assessment. Suggestions are given for strengthening the cold food chain risk-assessment system to provide a decision-making reference to help regulatory authorities take risk prevention and control measures.

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“…Zuo et al [ 24 ] propose using the public opinion text of food reviews as the analysis object to screen risky stores. Geng et al propose both [ 25 , 26 ] used the AHP-EW algorithm to generate a combined risk value for each sample and then combined it with a machine learning model for risk prediction. On this basis, Wang et al [ 27 ] used integrated learning techniques to improve the accuracy of the prediction models.…”

Section: Related Workmentioning

confidence: 99%

Anomaly Score-Based Risk Early Warning System for Rapidly Controlling Food Safety Risk

Zuo

Aysa

et al. 2022

Foods

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Food safety is a high-priority issue for all countries. Early warning analysis and risk control are essential for food safety management practices. This paper innovatively proposes an anomaly score-based risk early warning system (ASRWS) via an unsupervised auto-encoder (AE) for the effective early warning of detection products, which classifies qualified and unqualified products by reconstructing errors. The early warning analysis of qualified samples is carried out by early warning thresholds. The proposed method is applied to a batch of dairy product testing data from a Chinese province. Extensive experimental results show that the unsupervised anomaly detection model AE can effectively analyze the dairy product testing data, with a prediction accuracy and fault detection rate of 0.9954 and 0.9024, respectively, within only 0.54 s. We provided an early warning threshold-based method to conduct the risk analysis, and then a panel of food safety experts performed a risk revision on the prediction results produced by the proposed method. In this way, AI improves the panel’s efficiency, whereas the panel enhances the model’s reliability. This study provides a fast and cost-effective, food safety early warning method for detection data and assists market supervision departments in controlling food safety risk.

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Risk early warning of food safety using novel long short-term memory neural network integrating sum product based analytic hierarchy process

Cited by 16 publications

References 31 publications

An ensemble of AHP-EW and AE-RNN for food safety risk early warning

An ensemble of AHP-EW and AE-RNN for food safety risk early warning

Data-Driven Analysis of Risk-Assessment Methods for Cold Food Chains

Anomaly Score-Based Risk Early Warning System for Rapidly Controlling Food Safety Risk

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