This study presents possible applications of predictive microbiology to model the safety of mold-ripened cheeses with respect to bacteria of the species Listeria monocytogenes during (1) the ripening of Camembert cheese, (2) cold storage of Camembert cheese at temperatures ranging from 3 to 15°C, and (3) cold storage of blue cheese at temperatures ranging from 3 to 15°C. The primary models used in this study, such as the Baranyi model and modified Gompertz function, were fitted to growth curves. The Baranyi model yielded the most accurate goodness of fit and the growth rates generated by this model were used for secondary modeling (Ratkowsky simple square root and polynomial models). The polynomial model more accurately predicted the influence of temperature on the growth rate, reaching the adjusted coefficients of multiple determination 0.97 and 0.92 for Camembert and blue cheese, respectively. The observed growth rates of L. monocytogenes in mold-ripened cheeses were compared with simulations run with the Pathogen Modeling Program (PMP 7.0, USDA, Wyndmoor, PA) and ComBase Predictor (Institute of Food Research, Norwich, UK). However, the latter predictions proved to be consistently overestimated and contained a significant error level. In addition, a validation process using independent data generated in dairy products from the ComBase database (www.combase.cc) was performed. In conclusion, it was found that L. monocytogenes grows much faster in Camembert than in blue cheese. Both the Baranyi and Gompertz models described this phenomenon accurately, although the Baranyi model contained a smaller error. Secondary modeling and further validation of the generated models highlighted the issue of usability and applicability of predictive models in the food processing industry by elaborating models targeted at a specific product or a group of similar products.
Summary Behaviour of Yersinia enterocolitica in mould‐ripened Camembert‐type cheese during storage at temperature range 3–15 °C was evaluated and mathematically described. The Baranyi and Gompertz models were adjusted to the results of the study to calculate the growth rate (GR) and lag time (LT) for Y. enterocolitica at each temperature. Goodness of fit was assessed by calculating the Akaike information criterion (AIC) and mean square error (MSE). Square root models were constructed which described the relations between GR, LT and applied storage temperature. The secondary models were mathematically validated based on the results generated by ComBase Predictor. Moreover, generated models were validated using external, independent data from ComBase database. Based on this, it was found that the square root models of Ratkowsky constructed on GR that were determined based on the Baranyi and Roberts model most accurately described the behaviour of Y. enterocolitica in Camembert‐type cheese during storage under refrigerated conditions.
Raw skim milk was subjected to different heat treatments: thermization (65°C, 20 s), pasteurization (72°C, 15 s), and no heat treatment (milk was microfiltered using 1.4-µm membranes at 50°C for bacteria removal; 1.4 MF). The milk (thermized, pasteurized, and 1.4 MF) was cooled and stored at 2°C until processing (at least 24 h) with cold (~6°C) microfiltration using a benchtop crossflow pilot unit (Pall Membralox XLAB 5, Pall Corp., Port Washington, NY) equipped with 0.1-µm nominal pore diameter ceramic Membralox membrane (ET1-070, α-alumina, Pall Corp.). The flux was monitored during the process, and β-casein transmission and removal were calculated. The study aimed to indicate the conditions that should be applied to maximize β-casein passage through the membrane during cold microfiltration (5.6 ± 0.4°C) of skim milk. The proper selection of heat treatment parameters (temperature, time) of the feed before the cold microfiltration process will increase β-casein removal. It is not clear whether the difference in β-casein transmission between 1.4 MF, thermized, and pasteurized milk results from the effect of heat treatment conditions on β-casein dissociation from the casein micelles or on passage of β-casein through the membrane. The values of the major parameters (permeation flux and tangential flow velocity, through the wall shear stress) responsible for a proper membrane separation process were considerably lower than the critical values. It seems that the viscosity of the retentate has a great effect on the performance of the microfiltration membranes for protein separation at refrigerated temperatures.
The growth of Listeria monocytogenes was determined in Ultra-High-Temperature (UHT) dairy products (2% milk, 12 and 30% cream) at temperature range of 3-15C. Microbiological data were fitted to primary models (the Baranyi model, the modified Gompertz and logistic functions). The goodness-of-fit of primary models was analyzed by calculating mean square error and Akaike's information criterion. Baranyi model yielded the most accurate adjustment and the growth rates generated by this model were used for further mathematical analyses. Analysis of variance was used to check if the fat content significantly (P < 0.05) influences the behavior of L. monocytogenes. No statistical differences were noted in the behavior of the pathogen. Microbiological growth data were combined and secondary modeling was performed using Ratkowsky, Arrhenius and polynomial models. The latter model gave the best description and was further validated using accuracy (Af) and bias (Bf) factors, as well as data from ComBase database and COMBASE Predictor. PRACTICAL APPLICATIONSMathematical models that describe the behavior of microorganisms, especially foodborne pathogens, in a particular product or group of food products with similar characteristics, pose a perspective of using predictive microbiology in order to increase the food safety. Application of predictive models is in agreement with Codex Alimentarius Commission and UE regulations in a risk analysis area. Presented results can be used by food manufacturers in food product development process, as well as a tool to support food safety assurance systems. Moreover, predictive models find practical application in Hazard Analysis and Critical Control Point (HACCP) plans providing useful information on the determination of critical control points (CCPs) and the estimation of critical limits at CCPs. The assessment and management of safety, quality and shelf life of food products can be facilitated by application of mathematical predictive models. bs_bs_banner Journal of Food Safety
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