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, aw, 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.
Diffusion methods, including agar disk-diffusion and agar well-diffusion, as well as dilution methods such as broth and agar dilution, are frequently employed to evaluate the antimicrobial capacity of extracts and essential oils (EOs) derived from Origanum L., Syzygium aromaticum, and Citrus L. The results are reported as inhibition diameters (IDs) and minimum inhibitory concentrations (MICs), respectively. In order to investigate potential sources of variability in antimicrobial susceptibility testing results and to assess whether a correlation exists between ID and MIC measurements, meta-analytical regression models were built using in vitro data obtained through a systematic literature search. The pooled ID models revealed varied bacterial susceptibilities to the extracts and in some cases, the plant species and methodology utilised impacted the measurements obtained (p < 0.05). Lemon and orange extracts were found to be most effective against E. coli (24.4 ± 1.21 and 16.5 ± 0.84 mm, respectively), while oregano extracts exhibited the highest level of effectiveness against B. cereus (22.3 ± 1.73 mm). Clove extracts were observed to be most effective against B. cereus and demonstrated the general trend that the well-diffusion method tends to produce higher ID (20.5 ± 1.36 mm) than the disk-diffusion method (16.3 ± 1.40 mm). Although the plant species had an impact on MIC, there is no evidence to suggest that the methodology employed had an effect on MIC (p > 0.05). The ID–MIC model revealed an inverse correlation (R2 = 47.7%) and highlighted the fact that the extract dose highly modulated the relationship (p < 0.0001). The findings of this study encourage the use of extracts and EOs derived from Origanum, Syzygium aromaticum, and Citrus to prevent bacterial growth. Additionally, this study underscores several variables that can impact ID and MIC measurements and expose the correlation between the two types of results.
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