Modelling the fate of Listeria monocytogenes during manufacture and ripening of smeared cheese made with pasteurised or raw milk

An individual-based modeling (IBM) approach was developed to describe the behavior of a few Listeria monocytogenes cells contaminating smear soft cheese surface. The IBM approach consisted of assessing the stochastic individual behaviors of cells on cheese surfaces and knowing the characteristics of their surrounding microenvironments. We used a microelectrode for pH measurements and micro-osmolality to assess the water activity of cheese microsamples. These measurements revealed a high variability of microscale pH compared to that of macroscale pH. A model describing the increase in pH from approximately 5.0 to more than 7.0 during ripening was developed. The spatial variability of the cheese surface characterized by an increasing pH with radius and higher pH on crests compared to that of hollows on cheese rind was also modeled. The microscale water activity ranged from approximately 0.96 to 0.98 and was stable during ripening. The spatial variability on cheese surfaces was low compared to between-cheese variability. Models describing the microscale variability of cheese characteristics were combined with the IBM approach to simulate the stochastic growth of L. monocytogenes on cheese, and these simulations were compared to bacterial counts obtained from irradiated cheeses artificially contaminated at different ripening stages. The simulated variability of L. monocytogenes counts with the IBM/microenvironmental approach was consistent with the observed one. Contrasting situations corresponding to no growth or highly contaminated foods could be deduced from these models. Moreover, the IBM approach was more effective than the traditional population/macroenvironmental approach to describe the actual bacterial behavior variability. Since risk analysis emerged as the internationally recognized framework to improve food control systems, many risk assessments were published that evaluated the probabilities and severities of adverse health effects resulting from the exposure of consumers to pathogenic microorganisms present in foods. This is especially the case for the food-borne pathogen bacterium Listeria monocytogenes. Its ubiquitous nature and its ability to multiply in many foods during chilled storage fostered the development of quantitative microbial risk assessment aimed at ranking foods according to risk or predicting the impact of management options (1-6).The assessment of the microbial behavior of the pathogen is of the highest importance when performing these quantitative assessments, since listeriosis cases are predominantly linked to the consumption of highly contaminated foods (2), and the variability of this behavior is of paramount importance in the context of exposure assessment (7, 8). The major sources of variability affecting microbial responses in foods are the initial contamination level, the variability in processing factors, the variability in food characteristics and in the storage conditions, and the biological variability, i.e., the variability of microbial behavior.For several years, it has...

Section: Discussioncontrasting

confidence: 99%

mentioning

confidence: 99%

Combining Individual-Based Modeling and Food Microenvironment Descriptions To Predict the Growth of Listeria monocytogenes on Smear Soft Cheese

Ferrier

Hezard²,

Lintz³

et al. 2013

“…These data provide an interesting basis for assessing the final concentration in cheese under more realistic raw-milk contamination conditions, using a predictive microbiology and quantitative exposure assessment approach (60). It will be important to confirm the absence of a strain effect, as only a few strains of each serotype were tested here.…”

Section: Discussionmentioning

confidence: 99%

Behavior of Different Shiga Toxin-Producing Escherichia coli Serotypes in Various Experimentally Contaminated Raw-Milk Cheeses

Miszczycha

Perrin

Ganet

et al. 2013

e Shiga toxin-producing Escherichia coli (STEC) is an important cause of food-borne illness. The public health implication of the presence of STEC in dairy products remains unclear. Knowledge of STEC behavior in cheeses would help to evaluate the human health risk. The aim of our study was to observe the growth and survival of experimentally inoculated STEC strains in raw-milk cheeses manufactured and ripened according to five technological schemes: blue-type cheese, uncooked pressed cheese with long ripening and with short ripening steps, cooked cheese, and lactic cheese. Cheeses were contaminated with different STEC serotypes (O157:H7, O26:H11, O103:H2, and O145:H28) at the milk preparation stage. STEC growth and survival were monitored on selective media during the entire manufacturing process. STEC grew (2 to 3 log 10 CFU · g ؊1 ) in blue-type cheese and the two uncooked pressed cheeses during the first 24 h of cheese making. Then, STEC levels progressively decreased in cheeses that were ripened for more than 6 months. In cooked cheese and in lactic cheese with a long acidic coagulation step (pH < 4.5), STEC did not grow. Their levels decreased after the cooking step in the cooked cheese and after the coagulation step in the lactic cheese, but STEC was still detectable at the end of ripening and storage. A serotype effect was found: in all cheeses studied, serotype O157:H7 grew less strongly and was less persistent than the others serotypes. This study improves knowledge of the behavior of different STEC serotypes in various raw-milk cheeses.

“…The effect of undissociated lactic acid concentration on the growth of L. monocytogenes was applied with a separate modular function (12,16,21):…”

Section: Methodsmentioning

confidence: 99%

Modeling the Growth of Listeria monocytogenes in Soft Blue-White Cheese

Rosshaug

Detmer

Ingmer

et al. 2012

The aim of this study was to develop a predictive model simulating growth over time of the pathogenic bacterium Listeria monocytogenes in a soft blue-white cheese. The physicochemical properties in a matrix such as cheese are essential controlling factors influencing the growth of L. monocytogenes. We developed a predictive tertiary model of the bacterial growth of L. monocytogenes as a function of temperature, pH, NaCl, and lactic acid. We measured the variations over time of the physicochemical properties in the cheese. Our predictive model was developed based on broth data produced in previous studies. New growth data sets were produced to independently calibrate and validate the developed model. A characteristic of this tertiary model is that it handles dynamic growth conditions described in time series of temperature, pH, NaCl, and lactic acid. Supplying the model with realistic production and retail conditions showed that the number of L. monocytogenes cells increases 3 to 3.5 log within the shelf life of the cheese.