Baranyi Model Research Articles

Water Activity and Temperature Effects on Fungal Growth and Ochratoxin A Production by Ochratoxigenic Aspergillus carbonarius Isolated from Tunisian Grapes

This study investigated the effects of temperature (15 to 37 degrees C) and water activity (0.90 to 0.99) on the growth and production of ochratoxin A (OTA) by Aspergillus carbonarius cultured on synthetic nutrient medium (SNM) after 5 and 10 d of incubation. Total of 8 ochratoxigenic A. carbonarius, isolated from vineyards located in different regions of Tunisia, were used. Growth data were modeled by the flexible model of Baranyi and growth rates at each set of conditions were obtained. For both growth and OTA production, optimal water activity was 0.99; however, optimal temperature varied. The optimal temperature for growth was 30 degrees C. At 37 degrees C, the growth rate decreased significantly (P < 0.05). Maximum toxin production occurred at temperatures in the range of 15 to 25 degrees C with the optimum one depending on the isolate tested. Significant amounts of OTA were produced after only 5 d of incubation. Our results showed that A. carbonarius isolated from Tunisian grapes behave as those from European and Australian grapes, as reported in the literature, although some differences in trends for growth and OTA production were observed.

The Development of Predictive Growth Models for Total Viable Cells and Escherichia coli on Chicken Breast as a Function of Temperature

Abstract The aim of this research was to estimate the effect of temperature and develop predictive models for the growth of totalviable cells (TVC) and Escherichia coli (EC) on chicken breast under aerobic and various temperature conditions. The pri-mary models were determined by Baranyi model. The secondary models for the specific growth rate (SGR) and lag time(LT), as a function of storage temperature, were developed by the polynomial model. The initial contamination level ofchicken breasts was around 4.3 Log CFU/g of TVC and 1.0 Log CFU/g of E. coli . During 216 h of storage, SGR of TVCshowed 0.05, 0.15, and 0.54 Log CFU/g/h at 5, 15, and 25 o C. Also, the growth tendency of EC was similar to those of TVC.As storage temperature increased, the values of SGR of microorganisms increased dramatically and the values of LTdecreased inversely. The predicted growth models with experimental data were evaluated by B f , A f , RMSE , and R 2 . Thesevalues indicated that these developed models were reliable to express the growth of TVC and EC on chicken breasts. Thetemperature changes of distribution and showcase in markets might affect the growth of microorganisms and spoilage ofchicken breast mainly.

Development of a New Logistic Model for Microbial Growth in Foods

Mathematical models are essentially needed to quantitatively predict microbial growth in food products during their production and distribution. Recently we developed a new logistic model for microbial growth. The model is an extended logistic model, which shows a sigmoid curve on a semi-log plot. The model could precisely describe and predict bacterial growth at constant and dynamic temperatures in broth, on nutrient agar plates, and in pouched food. Prediction results with our model were very similar to those with the Baranyi model, which is well known worldwide. The model also predicted the amount of metabolites (toxins) that would be produced by a microorganism. Namely, with the growth model and the kinetics of staphylococcal enterotoxin A production, the amount of the toxins produced by Staphylococcus aureus in milk was successfully predicted. Our model could be a tool in the alert system and the quantitative risk assessment of harmful microbes in food.

Modelling of Escherichia coli O157:H7 growth at various storage temperatures on beef treated with electrolyzed oxidizing water

The influence of storage temperature (4, 10, 15, 20, 25, and 30 °C) on the growth of Escherichia coli O157:H7 in beef untreated (control) and treated by acidic electrolyzed oxidizing water (AcEOW) or slightly acidic electrolyzed oxidizing water (SAcEOW) was examined. A Baranyi model was employed to describe growth parameters such as specific growth rate (SGR) and lag time (LT) as a function of storage temperature. SGR increased and LT declined with rising temperatures in all samples. There were no significant differences between the SGR and LT values obtained from beef treated with AcEOW or SAcEOW. Secondary models were established for SGR and LT to evaluate the effects of storage temperature on the growth kinetics of E. coli O157:H7 in treated and untreated beef. Mathematical evaluation was carried out to validate the performance of the developed models.

Influence of the hot-fill water-spray-cooling process after continuous pasteurization on the number of decimal reductions and on Alicyclobacillus acidoterrestris CRA 7152 growth in orange juice stored at 35 °C

In this study, the influence of the hot-fill water-spray-cooling process after continuous pasteurization on the number of decimal reductions ( γ) and growth parameters (lag time; λ, ratio N f /N o; κ, maximum growth rate; μ) of Alicyclobacillus acidoterrestris CRA 7152 in orange juice stored at 35 °C were investigated. Two different inoculum levels of A. acidoterrestris CRA 7152 (10 2 and 10 3 spores/mL) in orange juice (11 0Brix, pH 3.7) and a Microthermics UHT-HTST pilot plant were used to simulate industrial conditions. Results have shown that regardless of the inoculum level (10 2 or 10 3 spores/mL), the pasteurization processes were unable to cause even 1 γ. Predictive modeling using the Baranyi model showed that only κ and time to reach 10 4spores/mL ( t10 4 — time to juice spoilage) were affected by the spore inoculum used ( p < 0.05). It has been concluded that A. acidoterrestris was able to survive the hot-fill process and to grow and spoil orange juice in 5–6 days when the final storage temperature was 35 °C.

PREDICTIVE MODELING AND VALIDATION OF GROWTH AT DIFFERENT TEMPERATURES OF BROCHOTHRIX THERMOSPHACTA

ABSTRACT The growth of Brochothrix thermosphacta affect by temperatures (0, 2, 5, 7 and 10C) were studied in laboratory medium. Growth curves were fitted using logistic, Gompertz and Baranyi models. Statistical characteristics like r2, mean square error, bias factor and accuracy factor were using for comparison of these models. Based on the criteria, the Gompertz described the data best, Baranyi performed the predicting best. The maximum growth rates obtained from primary model were then modeled as a function of temperature using the square root model. Statistically for the secondary model, the bias and accuracy factors are 0.9978, 0.9943 and 0.9712, and 1.0513, 1.0639 and 1.2225 for logistic, Gompertz and Baranyi, respectively, which may indicate that Baranyi model fitted and performed best of the others. The 95% confidence limits for a new prediction were estimated for each validation temperatures condition using a SPSS procedure.PRACTICAL APPLICATIONS Brochothrix thermosphacta was isolated from chilled pork used for growth modeling in broth (tryptone soya broth) at pH 7 and NaCl 0.5% (w/w), at various temperatures. Data obtained from experiments were then fitted by three types of primary models, to estimate the maximum growth rate. A secondary model was created, describing how the maximum growth rate responds to the temperatures. The goodness‐of‐fit and statistical characteristics of the equations were tested.

INACTIVATION OF VEROCYTOTOXIGENIC ESCHERICHIA COLI AND LISTERIA MONOCYTOGENES CO‐CULTURED WITH LACTOBACILLUS SAKEI IN A SIMULATED MEAT FERMENTATION MEDIUM

ABSTRACT The effect of bacteriocin producing Lactobacillus sakei 4413 (an isolate from a Greek dry‐fermented sausage) on inhibition of co‐cultured verocytotoxigenic Escherichia coli or Listeria monocytogenes strains was investigated. In the simulated meat fermentation medium (containing a mixture of nitrogen sources, glucose, sodium chloride and curing agents, with initial pH 6.5) incubated at 25C, L. sakei 4413 produced the highest concentration of bacteriocin in the exponential growth phase, reducing the pH of the medium to 4.3 and producing 0.54% w/v lactic acid. Three primary models (i.e., the Gompertz model, the Baranyi model and the Whiting‐Buchanan model) were evaluated for modeling survival curves of different verocytotoxigenic E. coli and L. monocytogenes strains co‐cultured with L. sakei 4413. The inactivation models evaluated indicated an acceptable degree of goodness‐of‐fit. However, the Whiting‐Buchanan model seems more accurate for fitting microbial inactivation curves, showing the highest R2values and lowest root mean square values in co‐cultures of L. sakei and E. coli or L. monocytogenes. Using the Whiting‐Buchanan model, a 4‐D (99.99%) inactivation of E. coli and L. monocytogenes strains was achieved after 50.9 and 48.7 h, respectively, in co‐cultures with L. sakei 4413 in the simulated meat fermentation medium incubated at 25C. In a synthetic medium containing a mixture of different nitrogen sources (i.e., peptone from meat, meat extract, yeast extract) the effect of temperature and glucose on the inhibitory action of L. sakei 4413 against E. coli O157 EDL‐932 was studied by central composite response surface methodology. The results indicate that the minimum time for a 4‐log decline (11.5 h) for E. coli O157 EDL‐932 in co‐culture with L. sakei 4413 can be achieved at 26C and 2% w/v glucose. The results indicate that L. sakei 4413 could be used as bio‐preservative or starter adjunct in meat fermentations in order to minimize the risk associated with the survival of the above foodborne pathogens.PRACTICAL APPLICATIONSThe development of indigenous starters is very promising in order to produce fermented sausages with high sanitary and sensory qualities. This work contributes to the development of a starter adjunct from an indigenous L. sakei (isolated from a traditional sausage manufactured by a Greek producer). L. sakei added in a model system inactivates verocytotoxigenic E. coli and L. monocytogenes. As L. sakei strain is well adapted to the meat environment, it will lead to sausages that do not present a sanitary risk.

Estimation of Shelf-life of Frankfurter Using Predictive Models of Spoilage Bacterial Growth

The aim of this research was to develop predictive models for the growth of spoilage bacteria (total viable cells, Pseudomonas spp., and lactic acid bacteria) on frankfurters and to estimate the shelf-life of frankfurters under aerobic conditions at various storage temperatures (5, 15, and <TEX>$25^{\circ}C$</TEX>). The primary models were determined using the Baranyi model equation. The secondary models for maximum specific growth rate and lag time as functions of temperature were developed by the polynomial model equation. During 21 d of storage under various temperature conditions, lactic acid bacteria showed the longest lag time and the slowest growth rate among spoilage bacteria. The growth patterns of total viable cells and Pseudomonas spp. were similar each other. These data suggest that Pseudomonas spp. might be the dominant spoilage bacteria on frankfurters. As storage temperature increased, the growth rate of spoilage bacteria also increased and the lag time decreased. Furthermore, the shelf-life of frankfurters decreased from 7.0 to 4.3 and 1.9 (d) under increased temperature conditions. These results indicate that the most significant factor for spoilage bacteria growth is storage temperature. The values of <TEX>$B_f$</TEX>, <TEX>$A_f$</TEX>, RMSE, and <TEX>$R^2$</TEX> indicate that these models were reliable for identifying the point of microbiological hazard for spoilage bacteria in frankfurters.

Modelling of mould/yeast growth on Korean braised lotus root cuts as a primary quality index in storage

AbstractMould/yeast growth on Korean braised lotus root cuts, a primary quality deterioration factor for shelf life determination, was monitored during storage at constant temperatures and was modelled as a function of temperature to estimate microbial spoilage under constant and/or dynamic storage conditions expected in a foodservice system. With help of a bootstrapping method to generate many resampled data sets of microbial plate counts, 1000 parameter sets for Baranyi's model in the form of differential equations were estimated. The temperature dependence of the primary model parameters was provided together with their confidence limits. When the temperature‐dependent parameters were incorporated into the differential equations for microbial growth, prediction of mould/yeast count change under constant and fluctuating temperature conditions could be provided with a confidence band close to the experimental data as a useful tool for the shelf life control.

Modeling the Effect of Marination and Temperature on Salmonella Inactivation during Drying of Beef Jerky

This study modeled the effect of drying temperature in combination with predrying marination treatments to inactivate Salmonella on beef jerky. Beef inside round slices were inoculated with Salmonella and treated with (1) nothing (C), (2) traditional marinade (M), or (3) dipped into a 5% acetic acid solution for 10 min before exposure to M (AM). After 24 h of marination at 4 degrees C, samples were dehydrated at 52, 57, or 63 degrees C. Total counts (tryptic soy agar supplemented with 0.1% sodium pyruvate, TSAP) and Salmonella (XLD agar) were enumerated after inoculation and at 0, 2, 4, 6, 8, and 10 h during drying. For calculation of death rates (DR, log CFU/cm(2)/h), shoulder period (h), low asymptote, and upper asymptote, cell counts from TSAP were fitted to the Baranyi model. The DRs were then further expressed as a function of storage temperature. Inactivation occurred without an initial lag phase (shoulder period), while correlation (R(2)) values of fitted curves were >/= 0.861. The DRs of C (-0.29 to -0.62) and M (-0.36 to -0.63) treatments were similar, while DRs of the AM treatment were higher (-1.22 to -1.46). The DRs were then fitted to a polynomial equation as a function of temperature. After validation, good (C and M) or acceptable (AM) model performances were observed (R(2)= 0.954 to 0.987; bias factors: 1.03 [C], 1.01 [M], 0.71 [AM]; accuracy factors: 1.05 [C], 1.06 [M], 1.41 [AM]). The developed models may be useful in selecting drying temperatures and times in combination with predrying treatments for adequate inactivation of Salmonella in beef jerky.

Modelling the growth ofWeissella cibariaas a function of fermentation conditions

To investigate the effect of pH, water activity (a(w)) and temperature on the growth of Weissella cibaria DBPZ1006, a lactic acid bacterium isolated from sourdoughs. The kinetics of growth of W. cibaria DBPZ1006 was investigated during batch fermentations as a function of pH (4.0-8.0), a(w) (0.935-0.994) and temperature (10-45 degrees C) in a rich medium. The growth curve parameters (lag time, growth rate and asymptote) were estimated using the dynamic model of Baranyi and Roberts (1994. A dynamic approach to predicting bacterial growth in food. Int J Food Microbiol23, 277-294). The effect of pH, a(w) and temperature on maximum specific growth rate (micro(max)) were estimated by fitting a cardinal model. Micro(max) under optimal conditions (pH = 6.6, a(w) = 0.994, T = 36.3 degrees C) was estimated to be 0.93 h(-1). Minimum and maximum estimated pH and temperature for growth were 3.6 and 8.15, and 9.0 degrees C and 47.8 degrees C, respectively, while minimum a(w) was 0.918 (equivalent to 12.2% w/v NaCl). Weissella cibaria DBPZ1006 is a fast-growing heterofermentative strain, which could be used in a mixed starter culture for making bread. This is the first study reporting the modelling of the growth of W. cibaria, a species that is increasingly being used as a starter in sourdough and vegetable fermentations.

Modelling the growth of Listeria monocytogenes in fresh green coconut ( Cocos nucifera L.) water

The behaviour of Listeria monocytogenes in the fresh coconut water stored at 4 °C, 10 °C and 35 °C was studied. The coconut water was aseptically extracted from green coconuts ( Cocos nucifera L.) and samples were inoculated in triplicate with a mixture of 5 strains of L. monocytogenes with a mean population of approximately 3 log 10 CFU/mL. The kinetic parameters of the bacteria were estimated from the Baranyi model, and compared with predictions of the Pathogen Modelling Program so as to predict its behaviour in the beverage. The results demonstrated that fresh green coconut water was a beverage propitious for the survival and growth of L. monocytogenes and that refrigeration at 10 °C or 4 °C retarded, but did not inhibit, growth of this bacterium. Temperature abuse at 35 °C considerably reduced the lagtimes. The study shows that L. monocytogenes growth in fresh green coconut water is controlled for several days by storage at low temperature, mainly at 4 °C. Thus, for risk population this product should only be drunk directly from the coconut or despite the sensorial alterations should be consumed pasteurized.

Control of Listeria monocytogenes on frankfurters by dipping in hops beta acids solutions.

Hops beta acids (HBA) are parts of hops flowers used in beer brewing and have shown antilisterial activity in bacteriological broth. The U.S. Department of Agriculture, Food Safety and Inspection Service has approved HBA for use to control Listeria monocytogenes on ready-to-eat meat products. This study evaluated the effects of HBA as dipping solutions to control L. monocytogenes during storage of frankfurters. Frankfurters (two replicates and three samples each) were inoculated (1.9 +/- 0.1 log CFU/cm2) with L. monocytogenes (10-strain mixture), dipped (2 min, 25 +/- 2 degrees C) in HBA solutions (0.03, 0.06, and 0.10%) or distilled water, and then vacuum packaged and stored at 4 or 10 degrees C for up to 90 and 48 days, respectively. Samples were periodically analyzed for microbial survival and growth on tryptic soy agar plus 0.6% yeast extract and PALCAM agar. Dipping in HBA solutions caused immediate L. monocytogenes reductions (P < 0.05) of 1.3 to 1.6 log CFU/cm2, whereas distilled water reduced counts by 1.0 log CFU/cm2. Pathogen growth was completely suppressed (P < 0.05) for 30 to 50 (4 degrees C) or 20 to 28 (10 degrees C) days on frankfurters dipped in HBA solutions, with antilisterial effects increasing with higher concentrations (0.03 to 0.10%). Fitting the data with the Baranyi model confirmed that the lag-phase duration of the pathogen was extended, and the growth rate was decreased on samples dipped in HBA solutions. Therefore, HBA may be considered for use to improve the microbial safety of ready-to-eat meat products, provided that future studies show no adverse effects on sensory qualities and that their use is economically feasible.

예측미생물을 이용한 미강식이섬유 함유 프랑크푸르터 소시지의 유통기한 설정

Predictive modeling was applied to study the growth of microorganisms related to spoilage in frankfurter sausage containing various levels of dietary fiber (0, 1, 2, and 3%) from rice bran and to estimate its shelf-life. Using the Baranyi model, total viable cells, anaerobic and psychrotrophic bacteria were measured during 35 days of cold storage (). The lag times (LT) demonstrated by control and treatment groups were 6.28, 623, 6.24, and 6.25 days, respectively. The growth rate of total viable cells in each group were 0.95, 0.91, 0.92, and 0.91 (Log CFU/g/day), respectively. The anaerobic and psychrotrophic bacteria had lower initial () and maximal bacterial counts () than total viable cells. Also, the anaerobic and psychrotrophic bacteria possessed lower growth rate and longer lag time than total viable cells. The estimated shelf-life of frankfurter containing rice bran fiber by the growth rate of total viable cells was 7.8, 7.9, 7.9, and 7.7 days, respectively. There were no significant differences in shelf-life as a function of fiber content. In other words, the addition of dietary fiber in sausage did not show the critically hazardous results in growth of microorganism. The 12 predictive models were then characterized by high , and small RMSE. Furthermore, and values showed a very close relationship between the predictive and observed data.

Field Validation of Predictive Models for the Growth of Lactic Acid Bacteria in Acidic Cheese-Based Greek Appetizers

A microbial model was developed for spoilage of two acidic Greek appetizers, namely, tyrosalata (TS) and tyrokafteri (TK), with pH values of 4.34 to 4.50 and 4.22 to 4.38, respectively. The specific spoilage organisms of these products were lactic acid bacteria (LAB), which dominated during storage, while yeasts, whenever present, remained at low levels (1 to 2 log CFU/g). Correlations of LAB populations with changes in pH and sensory characteristics indicated that the spoilage level of LAB ranged from 8.1 to 8.6 log CFU/g for both products. TK showed a relatively higher microbial stability than did TS. The growth of LAB was modeled with the Baranyi model, while their maximum specific growth rates were further modeled as a function of temperature with square-root model and Arrhenius equations for each appetizer. The validation of the model was performed under nonisothermal conditions in the laboratory and in a field validation trial with temperature logging during distribution of individual packages in the chill supply chain, including transportation from the plant to the distribution center, retail display, and household refrigerators. Models for both appetizers showed satisfactory agreement with data, with a slight tendency of overprediction of LAB in TS. The field validation process also confirmed the higher stability of TK over TS. The developed models may serve as a useful tool for monitoring the microbiological quality of such complex products and manage their distribution. Furthermore, depending on the seasonal variation of chill chain conditions, reassessment of shelf life may be performed.

Microbial Quality Change Model of Korean Pan-Fried Meat Patties Exposed to Fluctuating Temperature Conditions

Aerobic bacterial growth on Korean pan?fried meat patties as a primary quality deterioration factor was modeled as a function of temperature to estimate microbial spoilage on a real?time basis under dynamic storage conditions. Bacteria counts in the stretch?wrapped foods held at constant temperatures of 0, 5, 10 and 15℃ were measured throughout storage. The bootstrapping method was applied to generate many resampled data sets of mean microbial counts, which were then used to estimate the parameters of the microbial growth model of Baranyi & Roberts in the form of differential equations. The temperature functions of the primary model parameters were set up with confidence limits. Incorporating the temperature dependent parameters into the differential equations of bacterial growth could produce predictions closely representing the experimental data under constant and fluctuating temperature conditions.

Modeling survival of high hydrostatic pressure treated stationary- and exponential-phase Listeria innocua cells

Abstract High Hydrostatic Pressure (HHP) inactivation (325–400 MPa; 0–20 min; maximum temperature 30 °C) of cells of Listeria innocua CECT 910 was studied in two different growth phases (exponential and stationary), and the corresponding survival curves were obtained for each case. The curves were fitted to two nonlinear models, the modified Gompertz equation and the Baranyi model. The kinetic constants calculated for both models, µmax and kmax, indicated that cells in exponential growth phase were more sensitive to pressure than those in stationary phase. Both mathematical models were suitable for describing L. innocua HHP survival curves, rendering kinetic constants that increased with increasing pressure. When considering the experimental models validation, both Gompertz and Baranyi predicted in a similar way, however Baranyi had slightly lower Af (Accuracy factor) and Bf (Bias factor) values, which indicated better prediction values. In summary, both mathematical models were perfectly valid for describing L. innocua inactivation kinetics under HHP treatment. Industrial relevance The mathematical models for inactivation and growth of microorganisms are the foundation of predictive microbiology and are used in risk assessments procedures as part of the food safety management system. Besides, these models together with those applied to inactivation of enzymes and destruction of quality factors are essential to optimize processes and thus to lay the foundations for industrial processing. It is therefore necessary to identify generally applicable kinetic models that will produce primary and secondary kinetic parameters and are statistically reliable as a key tool to predict the behaviour of microorganisms, enzymes and quality factors after processing.

Modelling and predicting the effect of temperature, water activity and pH on growth ofStreptococcus iniaein Tilapia

To evaluate and model the growth of Streptococcus iniae affect by temperatures (10-45 degrees C), water activity (A(w); 0.995-0.957), and pH (5-8). Temperatures, A(w), and pH were adjusted. The behaviour of S. iniae was studied and modelled. Growth curves were fitted by using logistic, Gompertz, and Baranyi models. The maximum growth rates obtained from the primary model were then modelled as a function of temperature, A(w), and pH using the Belehradek-type models for secondary model. The optimum values for growth were found to be in the range of 35-40 degrees C, A(w) 0.995-1, and pH 6-7. The statistical characteristics of the models were validated by r(2), mean square error, bias, and accuracy factors. The results of validation indicated that Baranyi model performed the best. The effect of temperature, A(w)/NaCl, pH control of S. iniae in tilapia could be satisfactorily predicted under current experimental conditions, and the proposed models could serve as a tool for this purpose. The suggested predictive model can be used for risk assessment concerning S. iniae in tilapia.

Modeling Survival of Listeria monocytogenes in the Traditional Greek Soft Cheese Katiki

In the present work, survival of Listeria monocytogenes in the traditional Greek soft, spreadable cheese Katiki was studied throughout the shelf life of the product. Samples of finished cheese were inoculated with a cocktail of five L. monocytogenes strains (ca. 6 log CFU g−1) and stored at 5, 10, 15, and 20°C. Acid-stress adaptation or cross-protection to the same stress was also investigated by inoculation of acid-adapted cells in the product. The results showed that pathogen survival was biphasic. Various mathematical equations (Geeraerd, Cerf, Albert-Mafart, Whiting, Zwietering, and Baranyi models) were fitted to the experimental data. A thorough statistical analysis was performed to choose the best model. The Geeraerd model was finally selected, and the results revealed no acid tolerance acquisition (no significant differences, P > 0.05, in the survival rates of the non–acid-adapted and acid-adapted cells). Secondary modeling (second-order polynomial with a0 = 0.8453, a1 = −0.0743, and a2 = 0.0059) of the survival rate (of sensitive population), and other parameters that were similar at all temperatures (fraction of initial population in the major population = 99.98%, survival rate of resistant population = 0.10 day−1, and initial population = 6.29 log CFU g−1), showed that survival of the pathogen was temperature dependent with bacterial cells surviving for a longer period of time at lower temperatures. Finally, the developed predictive model was successfully validated at two independent temperatures (12 and 17°C). This study underlines the usefulness of predictive modeling as a tool for realistic estimation and control of L. monocytogenes risk in food products. Such data are also useful when conducting risk assessment studies.

Dynamic Modeling of Listeria monocytogenes Growth in Pasteurized Vanilla Cream after Postprocessing Contamination

A product-specific model was developed and validated under dynamic temperature conditions for predicting the growth of Listeria monocytogenes in pasteurized vanilla cream, a traditional milk-based product. Model performance was also compared with Growth Predictor and Sym'Previus predictive microbiology software packages. Commercially prepared vanilla cream samples were artificially inoculated with a five-strain cocktail of L. monocytogenes, with an initial concentration of 102 CFU g−1, and stored at 3, 5, 10, and 15°C for 36 days. The growth kinetic parameters at each temperature were determined by the primary model of Baranyi and Roberts. The maximum specific growth rate (μmax) was further modeled as a function of temperature by means of a square root–type model. The performance of the model in predicting the growth of the pathogen under dynamic temperature conditions was based on two different temperature scenarios with periodic changes from 4 to 15°C. Growth prediction for dynamic temperature profiles was based on the square root model and the differential equations of the Baranyi and Roberts model, which were numerically integrated with respect to time. Model performance was based on the bias factor (Bf), the accuracy factor (Af), the goodness-of-fit index (GoF), and the percent relative errors between observed and predicted growth. The product-specific model developed in the present study accurately predicted the growth of L. monocytogenes under dynamic temperature conditions. The average values for the performance indices were 1.038, 1.068, and 0.397 for Bf, Af, and GoF, respectively for both temperature scenarios assayed. Predictions from Growth Predictor and Sym'Previus overestimated pathogen growth. The average values of Bf, Af, and GoF were 1.173, 1.174, and 1.162, and 1.267, 1.281, and 1.756 from Growth Predictor and Sym'Previus, respectively.