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From Cheese-Making to Consumption: Exploring the Microbial Safety of Cheeses through Predictive Microbiology Models

期刊

FOODS
卷 10, 期 2, 页码 -

出版社

MDPI
DOI: 10.3390/foods10020355

关键词

fermented foods; food safety; predictive mathematical modeling; foodborne pathogens; dairy products; outbreaks

资金

  1. EU PRIMA program
  2. Spanish Ministry of Science and Innovation [PCI2019-103453, PRIMA/0001/2018]
  3. Fundação para a Ciência e a Tecnologia [PRIMA/0001/2018] Funding Source: FCT

向作者/读者索取更多资源

Cheeses are traditional products consumed worldwide, but are also frequently implicated in foodborne outbreaks. Predictive microbiology models are important tools for estimating microbial behavior in cheeses, with Listeria monocytogenes being the main target of concern. Factors such as pH, a(w), lactic acid concentration, and temperature are key independent variables in these models.
Cheeses are traditional products widely consumed throughout the world that have been frequently implicated in foodborne outbreaks. Predictive microbiology models are relevant tools to estimate microbial behavior in these products. The objective of this study was to conduct a review on the available modeling approaches developed in cheeses, and to identify the main microbial targets of concern and the factors affecting microbial behavior in these products. Listeria monocytogenes has been identified as the main hazard evaluated in modelling studies. The pH, a(w), lactic acid concentration and temperature have been the main factors contemplated as independent variables in models. Other aspects such as the use of raw or pasteurized milk, starter cultures, and factors inherent to the contaminating pathogen have also been evaluated. In general, depending on the production process, storage conditions, and physicochemical characteristics, microorganisms can grow or die-off in cheeses. The classical two-step modeling has been the most common approach performed to develop predictive models. Other modeling approaches, including microbial interaction, growth boundary, response surface methodology, and neural networks, have also been performed. Validated models have been integrated into user-friendly software tools to be used to obtain estimates of microbial behavior in a quick and easy manner. Future studies should investigate the fate of other target bacterial pathogens, such as spore-forming bacteria, and the dynamic character of the production process of cheeses, among other aspects. The information compiled in this study helps to deepen the knowledge on the predictive microbiology field in the context of cheese production and storage.

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