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Human-model interaction-based decision support system for optimizing food safety assessment

文献类型： 外文期刊

作者： Jin, Canghong 1 ; Xiao, Yuanhong 1 ; Wu, Hao 3 ; Ji, Xiaofeng 4 ; Li, Guang 5 ; Shuai, Jiangbing 6 ; Yang, Pinfeng 5 ; Xiong, Lina 5 ;

作者机构： 1.Hangzhou City Univ, Huzhou St 51, Hangzhou 310015, Zhejiang, Peoples R China

2.Zhejiang Univ, Zheda Rd 38, Hangzhou 310012, Zhejiang, Peoples R China

3.Macau Univ Sci & Technol, Ave Wai Long, Taipa 999078, Macao, Peoples R China

4.Zhejiang Acad Agr Sci, 298 Middle Desheng Rd, Hangzhou 310021, Zhejiang Provin, Peoples R China

5.Beingmate Hangzhou Food Res Inst Co Ltd, 1 Weiye Rd, Hangzhou 310000, Zhejiang, Peoples R China

6.Zhejiang Acad Sci & Technol Inspect & Quarantine, 555 Jianshe Third Rd, Hangzhou 310016, Zhejiang, Peoples R China

关键词： Human-machine interaction; Decision-making system; Food safety risk ranking; Human-in-the-loop technology

期刊名称：FOOD RESEARCH INTERNATIONAL （ 影响因子：8.0； 五年影响因子：8.5 ）

ISSN： 0963-9969

年卷期： 2025 年 208 卷

页码：

收录情况： SCI

摘要： Real-world decision support systems (DSS) operate in a continuous cycle of data collection, annotation, and model optimization, heavily relying on high-quality data. However, acquiring such data, particularly in specialized fields, is often expensive and resource-intensive, presenting significant challenges. To mitigate these challenges, recent machine learning research has increasingly focused on integrating experimental data and expert knowledge into user-friendly tools. In this paper, we present a novel framework named the Model-Humaninteraction Risk Assessment (MHRA), which leverages human interaction and collaborative scenario construction to achieve better performance. We address the increasing demand for a 'Human in the loop (HITL)' approach, which ensures the updateability of expert system knowledge bases during the input, selection, calculation, and ranking phases. Furthermore, we highlight the contributions of a human interactive simulation model in developing enhanced systems to assist decision-makers in maximizing the accuracy and standardization of evaluation models while minimizing food safety risks. We demonstrate the practical application of our framework through an infant food assessment case study and discuss the model's strengths and limitations.

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