Microbiological predictive modeling and risk analysis based on the one-step kinetic integrated Wiener process
文献类型： 外文期刊
作者： Chen, Qian 1 ; Zhao, Zhiyao 1 ; Wang, Xiaoyi 1 ; Xiong, Ke 4 ; Shi, Ce 5 ;
作者机构： 1.Beijing Technol & Business Univ, Sch Artificial Intelligence, Beijing 100048, Peoples R China
2.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China
3.Beijing Technol & Business Univ, China Key Lab Light Ind Ind Internet & Big Data, Beijing 100048, Peoples R China
4.Beijing Technol & Business Univ, Beijing Technol Res Ctr Food Addit Engn, Beijing 100048, Peoples R China
5.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
关键词： Food safety; Microbiological predictive modeling; Risk analysis; One-step kinetic analysis; Wiener process
期刊名称：INNOVATIVE FOOD SCIENCE & EMERGING TECHNOLOGIES （ 影响因子：7.104； 五年影响因子：7.391 ）
ISSN： 1466-8564
年卷期： 2022 年 75 卷
页码：
收录情况： SCI
摘要： The actual growth-monitoring data of microbial hazards in food are characterized by uncertainty, accumulation, discreteness, and nonlinearity, and thus it is difficult to accurately predict and analyze food safety microbio-logical risks in real time. Hence, we propose an approach of microbiological predictive modeling and risk analysis based on the one-step kinetic integrated Wiener process (OS-WP). First, the microbial tertiary prediction model was directly constructed through one-step kinetic analysis. Then, the WP was integrated with a tertiary model for predictive modeling of the actual microbial stochastic growth. Second, an indicator, "remaining safety life" (RSL), was introduced to analyze the potential microbiological risk on the basis of the established prediction models. Finally, the maximum likelihood estimation was used obtaining the model parameters online, and for calculating the RSL value in real time. The OS-WP approach was applied to a case study of the mixed mildew hazard during wheat storage. For different datasets, the root mean square error (RMSE) of the microbiological predictive model was less than 1.5; the relative RMSE of the RSL prediction reached 6.77%; the running time was less than 0.6 s. The result showed that the proposed approach is effective and feasible in modeling the actual growth of microbial hazards in food and can achieve online risk analysis. It can provide valuable microbiological early warning information to risk-management and decision-making departments for ensuring food safety.
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