确保食品安全,特别是对于弱势群体,像婴儿和幼儿,需要识别和优先考虑食物链中的潜在危害。我们之前开发了一个基于网络的决策支持系统(DSS),通过结构化的五步过程来识别婴幼儿食品中的特定微生物危害(MHs)。本研究通过引入系统风险排名(RR)步骤,以七个标准对MH风险进行排名来进一步框架:过程生存,再污染,增长机会,膳食准备,危害食品协会证据,欧盟婴幼儿的食物消费习惯,和MH严重性。每个标准都给出了半定量或定量的评分或风险值,通过三种汇总方法为最终的MH风险计算做出贡献:半定量风险评分,半定量风险值,超越多准则决策分析(MCDA)。为了验证标准和排名方法,我们进行了一项案例研究,对婴儿配方奶粉中的MH风险进行排名,比较了三种风险排序方法的结果,并根据专家意见对排名结果进行了额外评估,以确保其准确性。结果表明,三种方法具有很强的一致性,持续排名非伤寒沙门氏菌和克罗恩杆菌属。和产志贺毒素的大肠杆菌是婴儿配方食品中最大的MH风险,微小的偏差。当MHs在初始危险识别步骤之后进行排序时,这三种方法都产生了几乎相同的MH排名,加强排名步骤和选定标准的可靠性。值得注意的是,与风险评分方法相比,风险值和MCDA方法提供了更多信息的MH排名.将风险值和风险评分方法实施到在线工具中,称为微生物危害风险调查决策支持系统(Mira-DSS),可在https://foodmicrologywur上获得。shinyapps.io/微生物_危害_RAnking/。总之,我们的框架可以对MH风险进行排名,促进干预比较和资源分配,以减轻婴儿食品中的MH风险,具有对更广泛食品类别的潜在适用性。
Ensuring food safety, particularly for vulnerable groups, like infants and young children, requires identifying and prioritizing potential hazards in food chains. We previously developed a web-based decision support system (DSS) to identify specific microbiological hazards (MHs) in infant and toddler foods through a structured five-step process. This study takes the framework further by introducing systematic risk ranking (RR) steps to rank MH risks with seven criteria: process survival, recontamination, growth opportunity, meal preparation, hazard-food association evidence, food consumption habits of infants and toddlers in the EU, and MH severity. Each criterion is given a semi-quantitative or quantitative score or risk value, contributing to the final MH risk calculation via three aggregation methods: semi-quantitative risk scoring, semi-quantitative risk value, and outranking multi-criteria decision analysis (MCDA). To validate the criteria and ranking approaches, we conducted a case study to rank MH risks in infant formula, compared the results of the three risk ranking methods, and additionally evaluated the ranking results against expert opinions to ensure their accuracy. The results showed strong agreement among the three methods, consistently ranking Salmonella non-Typhi and Cronobacter spp. and Shiga-toxin-producing Escherichia coli as the top MH risks in infant formulae, with minor deviations. When MHs were ranked after an initial hazard identification step, all three methods produced nearly identical MH rankings, reinforcing the reliability of the ranking steps and the selected criteria. Notably, the risk value and MCDA methods provided more informative MH rankings compared to the risk scoring method. The risk value and risk scoring methods were implemented into an online tool, called the MIcrobiological hazards risk RAnking decision support system (Mira-DSS), available at https://foodmicrobiologywur.shinyapps.io/MIcrobial_hazards_RAnking/. In conclusion, our framework enables the ranking of MH risks, facilitating intervention comparisons and resource allocations to mitigate MH risks in infant foods, with potential applicability to broader food categories.