Risk Classification Assessment and Early Warning of Heavy Metal Contamination in Meat Products

Zheng Wang, Yanping Gao (), Xuemei Xu, Wei Dong and Tongqiang Jiang
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Zheng Wang: National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100083, China
Yanping Gao: National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100083, China
Xuemei Xu: China International Electronic Commerce Center, Beijing 100083, China
Wei Dong: National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100083, China
Tongqiang Jiang: National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100083, China

Sustainability, 2023, vol. 15, issue 21, 1-18

Abstract: Risk classification assessment and early warning systems are indispensable tools and technologies in the realm of regulatory control. Evaluating and issuing early warnings regarding heavy metal contaminants in meat products play a pivotal role in ensuring public safety and maintaining societal stability. In this study, we focused on heavy metal pollutants such as lead, cadmium, chromium, and arsenic. We collected national inspection data for meat products from 20 provinces in 2020. Combining dietary structure data, toxicology information, and dietary exposure assessment methods, we constructed a risk assessment model for heavy metal contaminants in food. Furthermore, we employed an entropy weight-based analytic hierarchy process (AHP-EW) to classify the results of the risk assessment for heavy metal contaminants in food. This involved determining risk rating levels and thresholds. Finally, we constructed a multi-step food contaminant risk prediction model based on the Transformer framework. To validate the model’s performance, comparative assessments were conducted across 20 datasets using various models. The results clearly indicate that the Transformer model outperformed the others in 14 datasets, excelling in its ability to provide advanced warnings for heavy metal risks in meat products. This empowers relevant authorities to strengthen their regulatory oversight of meat products based on the procedures and models proposed in this study, ultimately enhancing the efficiency of food safety risk management.

Keywords: heavy metal; meat products; multi-step time series prediction; risk assessment; transformer (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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