Research on Risk Prediction of Condiments Based on Gray Correlation Analysis – Deep Neural Networks
Food safety is directly related to the health of the public, and the safety of condiments is also of great significance. In this study, a risk assessment model for condiments based on gray correlation analysis was established by using publicly available sampling data of soy sauce and vinegar in Chin...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Journal of Food Protection |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0362028X24002035 |
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| _version_ | 1841553909868396544 |
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| author | Miao Zhang Yiran Wan Haiyang He Yuanjia Hu Changhong Zhang Jingyuan Nie Yanlei Wu Kaiying Deng Xun Lei Xianliang Huang |
| author_facet | Miao Zhang Yiran Wan Haiyang He Yuanjia Hu Changhong Zhang Jingyuan Nie Yanlei Wu Kaiying Deng Xun Lei Xianliang Huang |
| author_sort | Miao Zhang |
| collection | DOAJ |
| description | Food safety is directly related to the health of the public, and the safety of condiments is also of great significance. In this study, a risk assessment model for condiments based on gray correlation analysis was established by using publicly available sampling data of soy sauce and vinegar in China. Risk indicator screening and data preprocessing were performed first, and the weight of each indicator was calculated by gray correlation analysis to formulate a comprehensive risk value label. Then, three machine learning models, Deep Neural Network (DNN), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost), were used to predict the comprehensive risk values. Finally, the fuzzy synthesis analysis was utilized to classify the risk level of the composite risk value. In this study, based on the analysis of 282 sets of soy sauce and 704 sets of vinegar samples, the trained DNN model has the optimal prediction performance, which can basically predict the comprehensive risk value and risk level of a sample by inputting the detection indexes of that sample. This method can provide a more reasonable basis for relevant departments to formulate risk control strategies. |
| format | Article |
| id | doaj-art-a533c56593ce41ec98fb71708d8ba212 |
| institution | Kabale University |
| issn | 0362-028X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Food Protection |
| spelling | doaj-art-a533c56593ce41ec98fb71708d8ba2122025-01-09T06:12:34ZengElsevierJournal of Food Protection0362-028X2025-01-01881100419Research on Risk Prediction of Condiments Based on Gray Correlation Analysis – Deep Neural NetworksMiao Zhang0Yiran Wan1Haiyang He2Yuanjia Hu3Changhong Zhang4Jingyuan Nie5Yanlei Wu6Kaiying Deng7Xun Lei8Xianliang Huang9Chongqing Yongchuan District Center for Disease Control and Prevention, No. 471, Huilong Avenue, Yongchuan District, Chongqing, ChinaSchool of Public Health, Chongqing Medical University, Chongqing 401334, ChinaSchool of Public Health, Chongqing Medical University, Chongqing 401334, ChinaSchool of Public Health, Chongqing Medical University, Chongqing 401334, ChinaSchool of Public Health, Chongqing Medical University, Chongqing 401334, ChinaSchool of Public Health, Chongqing Medical University, Chongqing 401334, ChinaChongqing Institute for Food and Drug Control, Chongqing 401121, China; Key Laboratory of Condiment Supervision Technology, State Administration for Market Regulation, Chongqing 401121, ChinaChongqing Institute for Food and Drug Control, Chongqing 401121, China; Key Laboratory of Condiment Supervision Technology, State Administration for Market Regulation, Chongqing 401121, ChinaSchool of Public Health, Chongqing Medical University, Chongqing 401334, China; Corresponding authors at: Chongqing Institute for Food and Drug Control, No.1, Chunlan 2nd Road, Yubei District, 401121, Chongqing, China (X. Huang) and School of Public Health, Chongqing Medical University, No. 61, University Town Middle Road, 401331 Chongqing, China (X. Lei).Chongqing Institute for Food and Drug Control, Chongqing 401121, China; Key Laboratory of Condiment Supervision Technology, State Administration for Market Regulation, Chongqing 401121, China; Corresponding authors at: Chongqing Institute for Food and Drug Control, No.1, Chunlan 2nd Road, Yubei District, 401121, Chongqing, China (X. Huang) and School of Public Health, Chongqing Medical University, No. 61, University Town Middle Road, 401331 Chongqing, China (X. Lei).Food safety is directly related to the health of the public, and the safety of condiments is also of great significance. In this study, a risk assessment model for condiments based on gray correlation analysis was established by using publicly available sampling data of soy sauce and vinegar in China. Risk indicator screening and data preprocessing were performed first, and the weight of each indicator was calculated by gray correlation analysis to formulate a comprehensive risk value label. Then, three machine learning models, Deep Neural Network (DNN), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost), were used to predict the comprehensive risk values. Finally, the fuzzy synthesis analysis was utilized to classify the risk level of the composite risk value. In this study, based on the analysis of 282 sets of soy sauce and 704 sets of vinegar samples, the trained DNN model has the optimal prediction performance, which can basically predict the comprehensive risk value and risk level of a sample by inputting the detection indexes of that sample. This method can provide a more reasonable basis for relevant departments to formulate risk control strategies.http://www.sciencedirect.com/science/article/pii/S0362028X24002035Deep neural networkFuzzy comprehensive analysisGray correlation analysisRisk predictionCondiments safety |
| spellingShingle | Miao Zhang Yiran Wan Haiyang He Yuanjia Hu Changhong Zhang Jingyuan Nie Yanlei Wu Kaiying Deng Xun Lei Xianliang Huang Research on Risk Prediction of Condiments Based on Gray Correlation Analysis – Deep Neural Networks Journal of Food Protection Deep neural network Fuzzy comprehensive analysis Gray correlation analysis Risk prediction Condiments safety |
| title | Research on Risk Prediction of Condiments Based on Gray Correlation Analysis – Deep Neural Networks |
| title_full | Research on Risk Prediction of Condiments Based on Gray Correlation Analysis – Deep Neural Networks |
| title_fullStr | Research on Risk Prediction of Condiments Based on Gray Correlation Analysis – Deep Neural Networks |
| title_full_unstemmed | Research on Risk Prediction of Condiments Based on Gray Correlation Analysis – Deep Neural Networks |
| title_short | Research on Risk Prediction of Condiments Based on Gray Correlation Analysis – Deep Neural Networks |
| title_sort | research on risk prediction of condiments based on gray correlation analysis deep neural networks |
| topic | Deep neural network Fuzzy comprehensive analysis Gray correlation analysis Risk prediction Condiments safety |
| url | http://www.sciencedirect.com/science/article/pii/S0362028X24002035 |
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