提高乳制品安全的预测建模和风险评估策略:综述

Prachi Pahariya, Awani Shrivastav, Tridib Kumar Goswami, Ruplal Choudhary
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引用次数: 0

摘要

乳制品行业是食品行业的重要组成部分,包括各种生的、预处理的和后处理的乳制品。由于与乳制品行业相关的致病性疫情的增加,对提供安全乳制品的关注正在增加。为了减轻与乳制品相关的风险,风险评估(RA)和预测建模起着至关重要的作用。这篇综述文章提供了RA的综合分析和管理这些风险的预测建模。风险评估提供了一种结构化的方法,通过危险识别、危险特征、暴露评估和风险特征来评估公共卫生风险。预测建模通过使用科学和数学方法来预测微生物在各种条件下的行为,帮助预防整个乳制品供应链的污染,从而补充了RA。本文强调了这些工具在实际场景中的应用,以提高食品安全预测的准确性。综上所述,将风险评估与预测建模相结合对于降低污染风险,确保乳制品的安全和质量至关重要。虽然已经取得了重大进展,但未来的研究应侧重于通过强大的数据集和先进的机器学习来提高模型精度,从而制定更有效的战略来保护公众健康。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Predictive modeling and risk assessment strategies for enhancing dairy product safety: A comprehensive review

Predictive modeling and risk assessment strategies for enhancing dairy product safety: A comprehensive review

The dairy industry is a crucial part of the food sector, encompassing a wide range of raw, pre-processed, and post-processed dairy products. The concern about delivering safe dairy products is increasing due to the rise in pathogenic outbreaks associated with the dairy industry. To mitigate the risks associated with dairy products, risk assessment (RA) and predictive modeling play vital roles. This review article provides a comprehensive analysis of RA and predictive modeling in managing these risks. Risk assessment offers a structured approach to evaluate public health risks through hazard identification, hazard characterization, exposure assessment, and risk characterization. Predictive modeling complements RA by using scientific and mathematical methods to anticipate microbial behavior under various conditions, aiding in the prevention of contamination throughout the dairy supply chain. This article emphasizes the application of these tools in real-world scenarios to improve the accuracy of food safety predictions. In conclusion, integrating risk assessment with predictive modeling is essential for mitigating contamination risks and ensuring the safety and quality of dairy products. While significant advancements have been made, future research should focus on enhancing model precision through robust data sets and advanced machine learning, leading to more effective strategies that protect public health.

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