Application of ANN in food safety early warning

Liu, Zhen; Meng, Lingyong; Zhao, Wei; Yu, Fengqi

doi:10.1109/icfcc.2010.5497450

Cited by 8 publications

(9 citation statements)

References 1 publication

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“…In contrast, the ANN is nonlinear and fault-tolerant and builds models that do not rely on expert experience and that can fit the data well and predict accurately [ 21 ]. As a result, ANN technology has been widely employed in the field of food safety warnings [ 9 ]. Samuel et al utilized the fuzzy analytic hierarchy (AHP) technique to calculate the overall weight of an attribute based on its individual contribution and to predict the patient’s high-frequency risk by training an artificial neural network (ANN) classifier [ 11 ].…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, establishing a good risk analysis model is the key to efficient risk early warning. Common methods for food safety risk analysis include gray relationship-based analysis [ 5 , 6 ], Bayesian network-based methods [ 7 , 8 ], machine learning-based methods [ 6 , 9 , 10 ], and artificial neural network-based methods [ 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

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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“…If the predicted data is near the threshold value error may occur. To overcome such problem data near threshold values are used during training process [10].…”

Section: Literature Surveymentioning

confidence: 99%

“…In Agriculture sector, the crop yield is depend upon various factors like temperature, soil, moisture, rainfall, humidity. We will understand the impact of above factors with respect to crop yield [4][5][6][7][8][9][10][11][12][13][14][15][16][17].…”

Section: Recommended Parametermentioning

confidence: 99%

Machine Learning Technique for Crop Recommendation in Agriculture Sector

Patil,

Saiyyad

2019

IJEAT

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Numerous efforts have been demonstrated through various innovations to lead towards the betterment of agriculture sector till now. Effective and innovative use of Science and Technology can help to improve crop quality and production, yield prediction and crop disease analysis. Agriculture sector provides various productions such as food, raw material for industry and has a significant impact on economy and employment of a country. The agriculture sector contains huge data with respect to factors affecting its input and output. With advances in technology, various data mining techniques have been introduced accordingly. Also different analytical models like Decision Tree, Random Forest, Support Vector Machine and Bayesian Neural Network are available for required analysis. These data mining techniques can be used to analyze the multidimensional, time specific data of agriculture sector to produce effective knowledge in support of the efforts to boost the economy. These methods can be further used to analyze soil, climate, moisture, humidity, temperature. In this paper, we used Naive Bayesian Classification technique to identify the class of crop and used the food grain dataset to analyze the technique over different attribute. Further we used Machine Learning approach for the accurate crop recommendation. Crop recommendation will help for effective decision making to the end user to take proper decision according to the output of the system.

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“…Food safety prediction here is defined as a model‐based process that seeks to predict future food safety events or outcomes by analyzing patterns from historical food safety and other related data. The process of setting up food safety monitoring plans, and particularly the parts relating to identifying the products and hazards that should be assessed, could benefit from early warning and predictive modeling approaches (Geng et al., 2017; Liu et al., 2010). Such approaches can make use of historically collected monitoring data, as well as previous experiences and other available information, and use these to provide an assessment on the food safety hazards and/or products that should be prioritized for monitoring, as well as on when and where monitoring should be performed along the food supply chain.…”

Section: Introductionmentioning

confidence: 99%

Application of machine learning to the monitoring and prediction of food safety: A review

Wang

Bouzembrak

Lansink

et al. 2021

Comp Rev Food Sci Food Safe

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Machine learning (ML) has proven to be a useful technology for data analysis and modeling in a wide variety of domains, including food science and engineering. The use of ML models for the monitoring and prediction of food safety is growing in recent years. Currently, several studies have reviewed ML applications on foodborne disease and deep learning applications on food. This article presents a literature review on ML applications for monitoring and predicting food safety. The paper summarizes and categorizes ML applications in this domain, categorizes and discusses data types used for ML modeling, and provides suggestions for data sources and input variables for future ML applications. The review is based on three scientific literature databases: Scopus, CAB Abstracts, and IEEE. It includes studies that were published in English in the period from January 1, 2011 to April 1, 2021. Results show that most studies applied Bayesian networks, Neural networks, or Support vector machines. Of the various ML models reviewed, all relevant studies showed high prediction accuracy by the validation process. Based on the ML applications, this article identifies several avenues for future studies applying ML models for the monitoring and prediction of food safety, in addition to providing suggestions for data sources and input variables.

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Application of ANN in food safety early warning

Cited by 8 publications

References 1 publication

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

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

Machine Learning Technique for Crop Recommendation in Agriculture Sector

Application of machine learning to the monitoring and prediction of food safety: A review

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