食物腐败
食品安全
风险分析(工程)
生物技术
生化工程
食品加工
食物链
风险评估
供应链
食品工业
生物
计算机科学
业务
食品科学
工程类
生态学
营销
遗传学
计算机安全
细菌
作者
Akanksha Joshi,Dinesh Kumar Bhardwaj,Abhishek Kaushik,Vijay K. Juneja,Pankaj Taneja,Sheetal Thakur,Neetu Kumra Taneja
标识
DOI:10.1016/j.foodres.2022.111323
摘要
With the increasing consumption of packaged and ready-to-eat food products, the risk of foodborne illness has drastically increased and so has the dire need for proper management. The conventional Microbial Risk Assessment (MRA) investigations require prior knowledge of process flow, exposure, and hazard assessment throughout the supply chain. These data are often generated using conventional microbiological approaches based either on shelf-life studies or specific spoilage organisms (SSOs), frequently overlooking crucial information such as antimicrobial resistance (AMR), biofilm formation, virulence factors and other physiological variations coupled with bio-chemical characteristics of food matrix. Additionally, the microbial risks in food are diverse and heterogenous, that might be an outcome of growth and activity of multiple microbial populations rather than a single species contamination. The uncertainty on the microbial source, time as well as point of entry into the food supply chain poses a constraint to the efficiency of preventive approaches and conventional MRA. In the last few decades, significant breakthroughs in molecular methods and continuously progressing bioinformatics tools have opened up a new horizon for risk analysis-based approaches in food safety. Real time polymerase chain reaction (qPCR) and kit-based assays provide better accuracy and precision with shorter processing time. Despite these improvements, the effect of complex food matrix on growth environment and recovery of pathogen is a persistent problem for risk assessors. The dairy industry is highly impacted by spoilage and pathogenic microorganisms. Therefore, this review discusses the evolution and recent advances in MRAmethodologies equipped with predictive interventions and "multi-omics" approach for robust MRA specifically targeting dairy products. It also highlights the limiting gap area and the opportunity for improvement in this field to ensure precision food safety.
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