Listeria monocytogenes Survival Model Validated in Simulated Uncooked-Fermented Meat Products for Effects of Nitrite and pH

Predictive modeling of the early stages of semi-solid food ripening: Spatio-temporal dynamics in semi-solid casein matrices

A mechanistic, spatio-temporal model to predict early stage semi-solid food ripening, exemplary for semi-solid casein matrices, was created using software based on the finite element method (FEM). The model was refined and validated by experimental data obtained during 8 wk of ripening of a casein matrix that was inoculated by one single central injection of starter culture. The resulting spatio-temporal distributions of lactococci strains, lactose, lactic acid/lactate and pH allowed us to optimize a number of parameters of the predictive model. Using the optimized model, the agreement between simulation and experiment was found to be satisfactory, with the pH matching best. The predictive model unveiled that effective diffusion of substrate and metabolites were crucial for an eventual homogeneous distribution of the measured substances. Hence, while using the optimized parameters from the single injection model, an injection technology for starter culture to inoculate and ferment casein matrices homogeneously was developed by means of solving another optimization problem with respect to injection positions. The casein matrix inoculated by the proposed injection pattern (21 injections, distance = 19 mm) showed sufficient homogeneity (bacterial activity and pH distribution) after the early stages of ripening, demonstrating the potential of application of the injection technology for fermentation of casein-based foods e.g. cheese.

Modeling of starter cultures growth for improved Thai sausage fermentation and cost estimating for sausage preparation and transportation

The purpose of this study was to improve Thai fermented sausage flavor by adding starter cultures (i.e., Pediococcus pentosaceus, Pediococcus acidilactici, Weissella cibaria, Lactobacillus plantarum, Lactobacillus pentosus, and Lactobacillus sakei) as compared with naturally fermented sausage. The predictive mathematical models for growth of P. acidilactici and natural lactic acid bacteria (LAB) in Thai fermented sausage were developed to obtain specific prepared sausage quality. Furthermore, comparisons of sausage preparation and transportation cost between nonrefrigerated and refrigerated trucks were studied. The concentration of 3-methyl-butanoic acid synthesized from LAB inoculated sausage was higher than in the control sample which contributed to the flavor forming. Moreover, the proposed unstructured kinetic models of Thai fermented sausage substrates and products describing the consumption of total protein and glucose, and the production of nonprotein nitrogen responsible for flavor enhancer, lactic acid and formic acid concentration were successfully fitted with two selected experimental data sets of the in situ fermentation of Thai fermented sausage. Finally, the transportation of inoculated sausages in a nonrefrigerated truck by combining fermentation process and transportation was more cost efficient for delivering sausages in a long distance.

The effect of micro-architectural structure of cabbage substratum and or background bacterial flora on the growth of Listeria monocytogenes

The effect of micro-architectural structure of cabbage (Brassica oleracea var. capitata L.) substratum and or background bacterial flora on the growth of Listeria monocytogenes as a function of incubation temperature was investigated. A cocktail mixture of Pseudomonas fluorescens, Pantoea agglomerans and Lactobacillus plantarum was constituted to a population density of approximately 5 log CFU/ml in order to pseudo-simulate background bacterial flora of fresh-cut cabbage. This mixture was co-inoculated with L. monocytogenes (approximately 3 log CFU/ml) on fresh-cut cabbage or in autoclaved cabbage juice followed by incubation at different temperatures (4-30 degrees C). Data on growth of L. monocytogenes were fitted to the primary growth model of Baranyi in order to generate the growth kinetic parameters of the pathogen. During storage, microbial ecology was dominated by P. fluorescens and L. plantarum at refrigeration and abuse temperature, respectively. At all temperatures investigated, lag duration (lambda, h), maximum specific growth rate (micro(max), h(-1)) and maximum population density (MPD, log CFU/ml) of L. monocytogenes were only affected by medium micro-architectural structure, except at 4 degrees C where it had no effect on the micro(max) of the pathogen. Comparison of observed values of micro(max) with those obtained from the Pathogen Modelling Program (PMP), showed that PMP overestimated the growth rate of L. monocytogenes on fresh-cut cabbage and in cabbage juice, respectively. Temperature dependency of micro(max) of L. monocytogenes, according to the models of Ratkowsky and Arrhenius, showed linearity for temperature range of 4-15 degrees C, discontinuities and linearity again for temperature range of 20-30 degrees C. The results of this experiment have shown that the constituted background bacterial flora had no effect on the growth of L. monocytogenes and that micro-architectural structure of the vegetable was the primary factor that limited the applicability of PMP model for predicting the growth of L. monocytogenes on fresh-cut cabbage. A major limitation of this study however is that nutrient profile of the autoclaved cabbage juice may be different from that of the raw juice thus compromising realistic comparison of the behaviour of L. monocytogenes as affected by micro-architectural structure.

Growth of Listeria monocytogenes on iceberg lettuce and solid media

The growth of pathogenic bacterium Listeria monocytogenes on fresh-cut iceberg lettuce under constant temperatures was modelled in order to investigate microbial safety during distribution of this vegetable. We examined the effects of several incubation temperatures, ranging from 5 to 25 degrees C, on bacterial growth. These data were fitted to the Baranyi model and the curves showed a high correlation coefficient at all temperature (R2 > 0.95). In addition, the native bacterial flora of the lettuce did not affect the growth rate of L. monocytogenes regardless of incubation temperature. However, the lag time was reduced at a ratio of native bacteria to inoculated L. monocytogenes (100:1) at low incubation temperatures (5 and 10 degrees C). Furthermore, the maximum population density (MPD) was increased at a low ratio of native to inoculated L. monocytogenes (1:1) at all incubation temperatures. These results were compared with the previous work published by [Buchanan, R.L., Stahl, H.G., Whiting, R.C., 1989. Effects and interactions of temperature, pH, atmosphere, sodium chloride, and sodium nitrite on the growth of Listeria monocytogenes. J. Food Prot. 52, 844-851] that is being developed at the US Department of Agriculture (USDA) Agricultural Research Service's Pathogen Modelling Program (PMP). The broth-based Buchanan model for L. monocytogenes was found to markedly deviate from the observed data. In order to investigate this discrepancy, we examined the effects of medium environment and nutrient content on L. monocytogenes growth using tryptic soy agar plates (TSAP) and agar plates (AP) containing 1.7% sucrose. The inoculated bacteria on both TSAP and AP showed slower growth rates than that predicted by the PMP. The MPD of bacteria grown on TSAP was similar to the PMP model ( approximately 9 log10 CFU/ml or plate (circle of diameter of 90 mm)) regardless of the incubation temperature. By contrast, the MPD observed on AP was approximately 4 log10 CFU lower than that observed on TSAP or predicted by the PMP. Both the growth rate and the MPD of L. monocytogenes on AP were similar to those on lettuce. These results suggest that the solid medium and poor nutrient content inhibited the growth of L. monocytogenes on lettuce. The growth rates of the inoculated L. monocytogenes on all media were described using Ratkowsky's simple square root model.

Assessment of mathematical models for predicting Staphylococcus aureus growth in cooked meat products

The growth of Staphylococcus aureus in commercially available vacuum-packaged cooked ham, turkey breast meat, and chicken breast meat stored at 2.3, 6.5, 10, 13.5, and 17.7 degrees C was studied. Growth rates observed in these food products were compared with those predicted on the basis of various growth models found in the literature and with those generated by the Pathogen Modeling Program and the Food MicroModel software using graphical and mathematical analysis for performance evaluation. In general, the models studied overestimated the growth of S. aureus. The Dengremont and Membré model most closely matched the observed behavior of S. aureus in ham and chicken breast meat, with bias factors of 1.56 and 1.09, respectively. The Eifert et al. model accurately described the growth of S. aureus in turkey breast meat, with a bias factor of 1.51. The remaining models provided safe predictions of the growth rate of S. aureus, but with poor accuracy. Predictive microbiology models have an immediate practical application in improving microbial food safety and quality and are very useful decision support tools, but they should not be used as the sole determinant of product safety.

Predictive food microbiology for the meat industry: a review

Predictive food microbiology (PFM) is an emerging multidisciplinary area of food microbiology. It encompasses such disciplines as mathematics, microbiology, engineering and chemistry to develop and apply mathematical models to predict the responses of microorganisms to specified environmental variables. This paper provides a critical review on the development of mathematical modelling with emphasis on modelling techniques, descriptions, classifications and their recent advances. It is concluded that the role and accuracy of predictive food microbiology will increase as understanding of the complex interactions between microorganisms and food becomes clearer. However the reliance of food microbiology on laboratory techniques and skilled personnel to determine process and food safety is still necessary.

A novel modelling approach for predicting microbial growth in a raw cured meat product stored at 3 degrees C and at 12 degrees C in air

To predict microbial growth during chill storage of a traditional Greek raw sausage, a numerical model was developed and validated. In our novel approach, the specific growth rate of each microbial population was calculated on the basis of the main microbial populations grown in the sausage. In addition, the specific destructive effect of the sausage ecosystem was introduced to evaluate microbial growth. The model was integrated by the Runge-Kutta method and the parameter values were optimised by the least squares method. Fitting of the model to the experimental data derived from four sausage batches stored aerobically at 3 and 12 degrees C successfully described the microbial growth kinetics in the sausage niche. Finally, the parameter values estimated by the fitting of the model on the data set from each batch were used to predict microbial growth in the other batches at both storage temperatures.

Model of the influence of time and mild temperature on Listeria monocytogenes nonlinear survival curves

Heat treatment has long been regarded as one of the most widely used and most effective means of destroying pathogens in food. Up to now the linear relationship between the death rate and the temperature has been used when choosing the best heat treatment to apply. However, the information given by this linear relationship is no longer sufficient when nonlinear survival curves are observed. Consequently, the agri-food industry needs a tool to choose the best mild heat treatment to apply in the case of nonlinear survival curves. This study deals with the temperature-induced death of Listeria monocytogenes CIP 7831 in the stationary phase of growth. Eleven temperatures were tested. With the proposed primary and secondary models good fits of our data were obtained. A model describing both the effect of the duration of treatment and the temperature on the logarithm of the number of survivors was then built. A clear increase in the precision of the estimation of the parameters was obtained with this model. Moreover, with this model a new graphical strategy to choose a mild heat increase regarding a maximal survivor number has been proposed.

Expanded models for the non-thermal inactivation of Listeria monocytogenes

Previously developed four-variable response surface models for describing the effects of temperature, pH/lactic acid, sodium chloride and sodium nitrite on the time to achieve a 4-log, non-thermal inactivation (t4D) of Listeria monocytogenes in aerobic, acidic environments were expanded to five-variable models that distinguish the effects of pH and acidulant concentration. A total of 18 new variable combinations were evaluated and the inactivation kinetics data appended onto a consolidation of two data sets from earlier studies. The consolidated data set, which included 315 inactivation curves representing 209 unique combinations of the five variables, was analysed by response surface analysis. The quadratic model without backward elimination regression was selected for further evaluation. Three additional quadratic models were generated using the concentrations of undissociated lactic and/or nitrous acids as variables in place of percentage lactic acid and sodium nitrite concentration. Comparison of predicted t4D values against literature values for various food systems indicated that the models provide reasonable initial estimates of the inactivation of L. monocytogenes. The models based on the concentration of undissociated lactic and nitrous acids support the hypothesis that antimicrobial activity is associated with this form of the compounds. Evaluation of several examples suggests that these models may be useful for predicting the equivalent of the compounds' "minimal inhibitory concentrations' for accelerating inactivation under various conditions.

Mathematical Models for the Effects of pH, Temperature, and Sodium Chloride on the Growth of Bacillus stearothermophilus in Salty Carrots

Estimating the shelf life and safety of any food product is an important part of food product development. Predictive food microbiology reduces the time and expense associated with conventional challenge and shelf life testing. The purpose of this study was to characterize and model germination, outgrowth, and lag (GOL) time and the exponential growth rate (EGR) of Bacillus stearothermophilus in salty carrot medium (SCM) as a function of pH, temperature, and NaCl concentration. B. stearothermophilus is a spore-forming thermophilic organism associated with flat sour spoilage of canned foods. A split-split plot design was used to measure the effects and interactions of pH (5.5 to 7.0), temperature (45 to 60(deg)C), and NaCl (0 to 1%) on the growth kinetics of B. stearothermophilus in SCM. A total of 96 experiments were analyzed, with individual curve parameters determined by using the Gompertz equation. Quadratic polynomial models for GOL time and EGR of B. stearothermophilus in terms of temperature, pH, and NaCl were generated by response surface analysis. The r(sup2) values for the GOL time and EGR models were 0.917 and 0.916, respectively. These models provide an estimate of bacterial growth in response to combinations of the variables studied within the specified ranges. The models were used to predict GOL times and EGRs for additional experimental conditions. The accuracy of these predictions validated the model's predictive ability in SCM.

Model for the survival of Staphylococcus aureus in nongrowth environments

A model was developed to estimate the survival times of Staphylococcus aureus in nongrowth environments. A four strain mixture of S. aureus was inoculated into BHI broth that had a lactate buffer with various combinations of pH (3-7) and lactate (0-1%), NaCl (0.5-20%) and NaNO2 (0-200 ppm) and stored at different temperatures (4-42 degrees C). At appropriate times the survivors were enumerated by sampling and spreading on TSA plates. The survival curves were modeled with two forms of a logistic equation and the D values were determined. Polynomial regression equations were then calculated to predict the effect of the environmental factors on the D values. Survival times were increased with higher pH values, lower temperatures, and lower nitrite and lactate concentrations. Added salt increased survival times until the salt concentrations exceeded that of most foods.