摘要
为了解决目前食品中微生物危害风险评估中模块化过程风险模型仅能评估当前风险而未能考虑流通领域的风险因素和危害溯源等缺陷,该文对食品中微生物危害的定量风险评估建模方法进行了改进。改进方法将操作环境、人员、设备等风险因素抽象为危害转移模块,设置控制模块表征控制措施对风险因素的控制作用,设置效益模块表征实施控制措施的成本和收益;采用贝叶斯网络模型结构,结合预测微生物学,通过贝叶斯推理估计食品处理过程中微生物危害的数量及其出现的概率。仿真分析表明,改进方法在实现食品中微生物危害风险评估的同时,在同一模型结构下能溯源危害被引入的风险因素源头,评估风险因素对食品产品安全的影响程度,管理者通过综合考虑控制效果和成本能够选择合适的风险控制措施。改进的风险评估建模方法对现有方法进行了补充,扩展了风险评估模型的功能,也为企业在生产流通过程中预防和管理安全风险提供有力的工具,具有重要的理论和应用价值。
Varieties of risk factors such as operating environment, personnel, equipment may bring microbiological hazards into foods. In order to effectively implement risk management,we used the modular process risk modeling framework to improve the quantitative microbiological risk assessment method. In this method, risk factors were abstracted as a hazard transfer modular. Food safety risk factors included the sources of raw materials, storage risk, operational personnel hygiene, and environmental contamination risks. The hazard transfer process described the contamination frequency and the probability distribution of microbial quantity that introduced into a product by environment, operating personnel, equipment and other risk factors. If risk did not exist, the amount of microbial number was zero. If risk existed, the probability distribution of microbial number was defined with discrete or continuous distribution function. The proportion transferred to a product was described by function g（x） with operating time, temperature, contact area and etc. The control process described varieties of control measures such as the use of different disinfection, the implementation of different test frequencies may be taken in the production. The prevention and control impacts of a measure were described by the probability and quantity change of microbial number introduced by risk factors in the model. The utility modular was used to characterize the consumption cost and gain of the control measure. A modular process risk model can be established by Bayesian network by the following three steps：1） Define processes, materials mixing and partitioning, and processing parameters, select risk factors that may introduce microbes into a product and affect the microbial dynamics; 2） Select the appropriate basic processes, and define the Bayesian network structure of risk model;3） Collect risk data, and define the conditional probability of each node in the model by analyzing risk data. Combined with predictive microbiology, the number and occurrence probability of microbiological hazards in each process can be estimated using Bayesian inference. This risk model is capable of assessing the risk situation and the impact of risk factors on food safety. It also can trace the source of microbial hazards. Once adding control processes to hazard transfer processes, and modifying the conditional probability of the corresponding hazard transfer processes nodes, the effectiveness of one or several control measures can be verified. In the model, parameters of nodes can be adjusted to make it easy to assess the impact degree of risk factors and control measures on food product safety. Using this improved modeling method, the origin of microbiology hazard can be traced. Through comparing the effectiveness and profit of different control measures preferred one or control combination can be determined.
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2014年第6期279-286,共8页
Transactions of the Chinese Society of Agricultural Engineering
基金
质检公益性行业科研专项(201310147)
关键词
风险评估
微生物
模型
食品安全
模块化建模
贝叶斯网络
预测微生物学
risk assessment
microorganism
models
food safety
modular modeling
Bayesian Belief Network
predictive microbiology
