Pathogen Modeling Program Research Articles

Effects of spore purity on the wet heat resistance of Clostridium perfringens, Bacillus cereus and Bacillus subtilis spores

Heat resistance of spores of Clostridium perfringens 8238 (Hobbs Serotype 2), Bacillus cereus NCTC 11143 (4810/72), and Bacillus subtilis PS533, an isogenic derivative of strain PS832 (a 168 strain) was determined in ground beef at 95 °C. Spore purification was by centrifugation and washing with sterile distilled water (dH2O), followed by sonication and then Histodenz centrifugation for B. subtilis and C. perfringens, and centrifugation and washing with sterile dH2O followed by Histodenz centrifugation for B. cereus. Bags containing inoculated beef samples were submerged in a temperature-controlled water bath and held at 95 °C for predetermined lengths of time. Surviving spore populations were enumerated by plating on mannitol egg yolk polymyxin agar (MYP) agar plates for B. cereus and B. subtilis, and on tryptose-sulfite-cycloserine agar (TSC) agar plates for C. perfringens. Survivor curves were fitted to linear, linear with tail, and Weibull models using the USDA Integrated Pathogen Modeling Program (IPMP) 2013 software. The Weibull model provided a relatively better fit to the data since the root mean square error (RMSE), mean square error (MSE), sum of squared errors (SSE), and Akaike information criterion (AIC) values were lower than the values obtained using the linear or the linear with tail models. Additionally, the Weibull model accurately predicted the observed D-values at 95 °C for the three spore-formers since the accuracy factor (Af) values ranged from 1.03 to 1.08 and the bias factor (Bf) values were either 1.00 or 1.01. Times at 95 °C to achieve a 3-log reduction decreased from 206 min for C. perfringens spores purified with water washes alone to 191 min with water washes followed by sonication and Histodenz centrifugation, from 7.9 min for B. cereus spores purified with water washes alone to 1.4 min with water washes followed by Histodenz centrifugation, and from 20.6 min for B. subtilis spores purified with water washes alone to 6.7 min for water washes followed by sonication and Histodenz centrifugation. Thermal-death-time values reported in this study will assist food processors to design thermal processes to guard against bacterial spores in cooked foods. In addition, clearly spore purity is an additional factor in spore wet heat resistance, although the cause of this effect is not clear.

Modelling the growth of Staphylococcus aureus on cooked broccoli under isothermal conditions.

In this study, we developed predictive models describing the growth of Staphylococcus aureus on cooked broccoli florets. A pool of 3.5 log CFU/g of five S. aureus strains were inoculated on 10 g broccoli portions. The samples were then stored at 10, 20, 30 and 37 °C, and colonies were enumerated at different time intervals. Baranyi and Roberts model was fitted to the data using a Bayesian Adaptive Markov Chain Monte Carlo for estimation of the growth parameters. S. aureus showed low growth at 10 °C on broccoli samples and at 20-37 °C interval, Baranyi and Roberts model fitted well to the experimental data (R2>0.97). Estimated growth parameters were correlated with the possibility of toxin production and indicate the potential presence of these biological hazards on contaminated broccoli after heat treatment. Additionally, linear regression was performed for growth rate as storage temperature function. This secondary model followed a linear tendency with R2=0.997 and was compared with two tertiary models (ComBase Predictor and Pathogen Modeling Program) and literature data, demonstrating similar growth rate values of both. These results can be helpful for food services and managers to establish food safety standards for S. aureus growth on cooked broccoli.

Contamination and changes of food factors during processing with modeling applications - Safety related issues

Chemical and microbiological contaminations of food during processing and preservation can result in foodborne illness outbreaks and/or poisoning. Chemical contaminations can occur through exposure of foods to illegal additives, pesticides and fertilizer residues, toxic compounds formed by chemical reactions, and are easier to control than illnesses caused by microorganisms. In general, chemical reactions in food can be described using the first-order kinetic models. Food quality factors related to nutrition, color, texture, etc. are typically affected by temperature, pH, moisture content, as well as microbial growth. However, modeling the growth and inactivation of microorganisms is much more difficult and complex than that for chemical reactions. If harmful microbes are not eliminated during processing, their survival and growth may cause spoilage and foodborne illness. Food safety intervention technologies such as microwave heating, and modified packaging were discussed, as well as issues related to cross-contamination of foods during processing. Microbial food safety can be enhanced by the development of growth and inactivation modeling tools, world-wide data sharing, and collaboration in which the Pathogen Modeling Program (PMP) and ComBase can be utilized.

Mathematical modeling Pseudomonas spp. growth and microflora composition variation in Agaricus bisporus fruiting bodies during chilled storage

The objective of this study was to determine the dominant microorganism in Agaricus bisporus fruiting bodies (ABFB) and to develop kinetic models to describe its growth. The variation in microflora composition in ABFB stored at 4 °C during storage was studied using 16S rDNA sequence analyses. ABFB presented complex microbial communities at the initial stage of storage (day 0), with the dominant microorganisms being Pseudomonas spp. along with small proportions of Pedobacter, Sphingobacterium, Bacillus, Corynebacterium, Lactobacillus, Sphingomonas, and Staphylococcus. On day 12, an increase of Pseudomonas spp. and a significant decrease of Pedobacter were observed. As the dominant spoilage microorganism during the storage, the relative abundance of Pseudomonas spp. showed an increasing trend. Additionally, samples inoculated with a cocktail of Pseudomonas spp., i.e., P. fluorescens, P. migulae, P. tolaasii, and P. agarici, were incubated at temperatures of 4, 10, 16, 20, 25, and 32 °C to assess their growth kinetics. Three primary models (Huang, Baranyi, and Reparameterized Gompertz model) and three secondary models (Huang square-root, Ratkowsky square-root, and Arrhenius-type model) were compared to evaluate the effect of temperature on bacterial growth using the Integrated Pathogen Modeling Program. The Reparameterized Gompertz model was a better fit than the Huang and Baranyi models to describe the growth of Pseudomonas spp., and had the lowest mean square error (MSE) and sum of squared errors (SSE) values, ranging from 0.012 to 0.291 and 0.059–1.783, respectively. Huang square-root model MSE and SSE values were 0.002 and 0.007, respectively, and its predicted minimum growth temperature was -0.72 °C. Therefore, the Huang square-root model was more suitable for describing the effect of temperature on growth of Pseudomonas spp. on ABFB. The models developed in this study can be used to evaluate the growth behavior of Pseudomonas spp. on ABFB and assess their shelf-life.

Thermal inactivation of Bacillus cereus spores during cooking of rice to ensure later safety of boudin

The heat resistance of a four-stain cocktail of Bacillus cereus was determined in rice. Bags containing inoculated rice samples with hot water were submerged in a temperature-controlled water bath and held at 90.5, 95, or 99 °C for predetermined lengths of time. Surviving spore population were enumerated by plating on mannitol egg yolk polymyxin agar. Survivor curves were fitted to linear and Weibull models using the USDA Integrated Pathogen Modeling Program (IPMP) 2013 software. While both models provided good fit to the data, Weibull model was relatively better since root mean square error values were lower (<0.32). The time durations to achieve a 4-log reduction were 256.26, 138.81 and 44.38 min at 90.5 °C, 95 °C, and 99 °C, respectively. Thermal-death-time values reported in this study will assist food processors to design lethality processes for the manufacture of foods incorporating cooked rice, such as boudin.

Synergistic effect of cinnamaldehyde on the thermal inactivation of Listeria monocytogenes in ground pork.

The aim of this study was to statistically evaluate the effect of a naturally food-derived cinnamaldehyde on the thermal inactivation of Listeria monocytogenes in ground pork. This study combined four concentrations of cinnamaldehyde (0, 0.1, 0.5, and 1.0% vol/wt) and four temperatures (55, 60, 65, and 70 ℃) to predict the thermal inactivation curves of L. monocytogenes. The Weibull model successfully described the primary thermal inactivation using the Integrated Pathogen Modeling Program. These results statistically proposed that the cinnamaldehyde supplementation in ground pork attenuates the thermo-tolerance of L. monocytogenes. The time for achieving a 5-log10 reduction of L. monocytogenes declined from 28.14 to 17.35 min at 55 ℃ when the ground pork sample was supplemented by 1% cinnamaldehyde, while the time declined from 1.95 to 0.34 min at 70 ℃. Thereafter, based on the 5.0-log10 lethality, secondary models were fitted by a selected polynomial model. The transmission electron microscopy revealed that cinnamaldehyde causes serious damage to membrane integrity and increases the occurrence of cell membrane rupture and leakage of cytoplasmic content under thermal treatment. Our model represents a mathematical tool that will help meat-product manufacturers to improve the efficacy of thermal processing ground pork supplemented with cinnamaldehyde.

The effects of grapefruit seed extract on the thermal inactivation of Listeria monocytogenes in sous-vide processed döner kebabs

The heat resistance (57.5–65 °C) of Listeria monocytogenes in vacuum packaged, cook-in-bag, döner kebabs that included 0.5 or 1% grapefruit seed extract (GSE) was determined. The packages were processed to an internal temperature of 57.5, 60, 62.5 or 65 °C in 1 h, held for a predetermined period of time, and then, ice chilled. The surviving cell population was determined by plating diluted samples onto tryptic soy agar overlaid with 10 ml of modified Oxford agar. Survivor curves were fitted to an equation based on the Weibull distribution using the USDA Integrated Pathogen Modeling Program 2013 software tool. The Weibull model consistently offered more accurate fit to all survivor curves based on the smaller root mean square error (<0.46). The times to 4-log reduction were 140, 62.38, 14.45 and 6.84 min at 57.5, 60, 62.5, and 65 °C, respectively. Supplementing döner kebab with 0.5–1% GSE rendered the pathogen more sensitive to the lethal effect of heat. The results provide the food industry with an option to supplement 0.5–1% GSE in sous vide processed döner kebab, that ensures adequate degree of protection against L. monocytogenes and at the same time, provide energy conservation and quality products.

Growth modeling of Uropathogenic Escherichia coli in ground chicken meat

Extraintestinal Pathogenic Escherichia coli (ExPEC), including Uropathogenic E. coli (UPEC), are common contaminants in poultry meat, and are a major pathogen associated with inflammatory bowel disease, ulcerative colitis, sepsis, and urinary tract infections. The purpose of this study was to determine the growth potential of UPEC in ground chicken meat. A multi-isolate cocktail of UPEC was inoculated into ground chicken meat 103−4 log CFU/g and incubated at 4, 10, 15, 22, and 30 °C. The USDA Integrated Pathogen Modeling Program (IPMP) was used to conduct mathematical modeling and validation of UPEC growth using the Huang Primary Model and the Huang Square Root Secondary Model. No growth occurred at 4 °C, while the lag phases were ca. 23.6, 11.5, 5.2, and 0.36 h at 10, 15, 22, and 30 °C. According to the model, the Tmin, the minimum temperature for UPEC growth in ground chicken, was 5.1 °C. The growth rates (μmax, ln CFU/g h−1) were ca. 0.06, 0.27, 0.48, and 0.90. Approximately 83.9% of the residual errors are between ±0.5 log CFU/g, suggesting that the predictive models and the associated kinetic parameters are sufficiently accurate in predicting the growth of UPEC in ground chicken. These models have been validated and can be used in risk assessment of ExPEC in poultry meat.

Predictive growth model of the effects of temperature on the growth kinetics of generic Escherichia coli in the Korean traditional rice cake product "Garaetteok".

In this study, a predictive growth model of generic Escherichia coli in Garaetteok at a range of storage temperatures (T, 10-40°C) was developed. The primary models of specific growth rate (SGR) and lag time (LT) fit well (R2≥0.985) using a Gompertz equation. Secondary polynomial models were obtained by non-linear regression and calculated as SGR=-0.01,570+0.0183T+0.000008T2; LT=43.2064-2.4824T+0.0355T2. The appropriateness of the secondary models was verified by mean square error (MSE; 0.0006 for SGR, 0.282 for LT), bias factor (B f ; 0.948 for SGR, 0.942 for LT), accuracy factor (A f ; 1.163 for SGR, 1.355 for LT), and coefficient of determination (r2; 0.986 for SGR, 0.996 for LT), and these models were found to be in good agreement with the experimental values used for validation. The secondary models developed in this study may thus be used as practical prediction models for generic E. coli growth in Garaetteok. These newly developed secondary models of SGR and LT for generic E. coli in Garaetteok may thus be incorporated into tertiary modeling programs such as the Korea Pathogen Modeling Program, in which they can easily be used to predict the growth kinetics of E. coli as a function of storage temperature. Ultimately, model developed in this study may be a vital tool for the reduction of E. coli levels in food production, processing, and distribution processes, which in turn will lead to enhanced safety of rice products.

Growth of Listeria monocytogenes in Thawed Frozen Foods

The growth characteristics of Listeria monocytogenes inoculated onto frozen foods (corn, green peas, crabmeat, and shrimp) and thawed by being stored at 4, 8, 12, and 20°C were investigated. The growth parameters, lag-phase duration (LPD) and exponential growth rate (EGR), were determined by using a two-phase linear growth model as a primary model and a square root model for EGR and a quadratic model for LPD as secondary models, based on the growth data. The EGR model predictions were compared with growth rates obtained from the USDA Pathogen Modeling Program, calculated with similar pH, salt percentage, and NaNO2 parameters, at all storage temperatures. The results showed that L. monocytogenes grew well in all food types, with the growth rate increasing with storage temperature. Predicted EGRs for all food types demonstrated the significance of storage temperature and similar growth rates among four food types. The predicted EGRs showed slightly slower rate compared with the values from the U.S. Department of Agriculture Pathogen Modeling Program. LPD could not be accurately predicted, possibly because there were not enough sampling points. These data established by using real food samples demonstrated that L. monocytogenes can initiate growth without a prolonged lag phase even at refrigeration temperature (4°C), and the predictive models derived from this study can be useful for developing proper handling guidelines for thawed frozen foods during production and storage.

Preliminary Quantitative Microbial Risk Assessment for Staphylococcus enterotoxins in fresh Minas cheese, a popular food in Brazil

The main objective of this work was to assess the risk associated with staphylococcal enterotoxins (SE) intoxication after the consumption of fresh Minas cheese by the Brazilian population. Coagulase-positive staphylococci data from 350 samples were obtained from monitoring programs, and were used as a proxy for S. aureus contamination, considering that 73% of the strains were toxigenic. The Combined Database for Predictive Microbiology (ComBase) and the Pathogen Modeling Program (PMP) models were used to predict S. aureus growth rate and lag-phase in fresh Minas cheese at different pH, salt concentration and storage temperature in a household refrigerator, up to 7 days before consumption. Change in storage temperature had the largest impact on the growth rate and lag-phase obtained from both models. Cumulative probability of SE intake events equal to or higher than the toxigenic dose of 100 ng were calculated using Monte Carlo simulations performed by the @Risk software. The toxic dose was exceeded at the 99.95th percentile of exposure in the ComBase model (upper bound) for the adult population, the lowest percentile identified in the study. The S. aureus initial concentration was the parameter that most impacted the output obtained by @risk, indicating the importance of good manufacturing practices for fresh Minas cheese production, and proper storage conditions at the point of sale. This preliminary assessment indicated that the risk of staphylococcal intoxications from the consumption of fresh Minas cheese by the Brazilian population is probably low. The study identified many data gaps that needs to be addressed to improve the risk assessment.

Predictive model for the growth kinetics of Staphylococcus aureus in raw pork developed using Integrated Pathogen Modeling Program (IPMP) 2013

A predictive model was performed to describe the growth of Staphylococcus aureus in raw pork by using Integrated Pathogen Modeling Program 2013 and a polynomial model as a secondary predictive model. S. aureus requires approximately 180h to reach 5–6 log CFU/g at 10°C. At 15°C and 25°C, approximately 48 and 20h, respectively, are required to cause food poisoning. Predicted data using the Gompertz model was the most accurate in this study. For lag time (LT) model, bias factor (Bf) and accuracy factor (Af) values were both 1.014, showing that the predictions were within a reliable range. For specific growth rate (SGR) model, Bf and Af were 1.188 and 1.190, respectively. Additionally, both Bf and Af values of the LT and SGR models were close to 1, indicating that IPMP Gompertz model is more adequate for predicting the growth of S. aureus on raw pork than other models.

Development of an absorbance-based response model for monitoring the growth rates of Arcobacter butzleri as a function of temperature, pH, and NaCl concentration

In this study, the growth of Arcobacter butzleri in poultry was evaluated as a function of storage temperature (5, 22.5, and 40°C), pH (5, 7, and 9), and NaCl concentration (0, 4, and 8%). A predictive model was developed using the absorbance-based response surface methodology to describe the growth rate. The primary model was obtained to predict a growth rate with a good fit (R2 ≥ 0.95), and the secondary model was obtained by nonlinear regression analysis and calculated as follows: Growth rate = −2.267274 – 0.024181 (Temp) + 0.6459384 (pH) + 0.1926227 (NaCl) + 0.0024661 (Temp × pH) – 0.001312 (Temp × NaCl) – 0.018802 (pH × NaCl) + 0.000467 (Temp2) – 0.041711 (pH2) – 0.007426 (NaCl2). Our data showed that the growth of A. butzleri can be completely inhibited at a pH of 5 (in the absence of NaCl, at 5°C) and at a pH of 9 (in the presence of 8% NaCl, at 5°C). The surface response model was statistically significant, with P < 0.0001, as evident from the Fisher F test and from coefficient determination (R2, 0.95). This model was also verified by the bias factor (Bf, 0.839), accuracy factor (Af, 1.343), and mean square error (MSE, 0.0138). The newly developed secondary models of growth rate for A. butzleri could possibly be incorporated into a tertiary modeling program such as Pathogen Modeling Program (U.S. Department of Agriculture [USDA]) and Food Micro Model (in the United Kingdom). As a result, they could be used to predict the growth kinetics of A. butzleri as a function of a combination of environmental factors. Ultimately, the developed model can be used to reduce A. butzleri in poultry production, processing, and distribution, thereby enhancing food safety.

Interactive Effects of Temperature, pH, and Water Activity on the Growth Kinetics of Shiga Toxin–Producing Escherichia coli O104:H4

The risk of non-O157 Shiga toxin-producing Escherichia coli strains has become a growing public health concern. Several studies characterized the behavior of E. coli O157:H7; however, no reports on the influence of multiple factors on E. coli O104:H4 are available. This study examined the effects and interactions of temperature (7 to 46°C), pH (4.5 to 8.5), and water activity (aw ; 0.95 to 0.99) on the growth kinetics of E. coli O104:H4 and developed predictive models to estimate its growth potential in foods. Growth kinetics studies for each of the 23 variable combinations from a central composite design were performed. Growth data were used to obtain the lag phase duration (LPD), exponential growth rate, generation time, and maximum population density (MPD). These growth parameters as a function of temperature, pH, and aw as controlling factors were analyzed to generate second-order response surface models. The results indicate that the observed MPD was dependent on the pH, aw, and temperature of the growth medium. Increasing temperature resulted in a concomitant decrease in LPD. Regression analysis suggests that temperature, pH, and aw significantly affect the LPD, exponential growth rate, generation time, and MPD of E. coli O104:H4. A comparison between the observed values and those of E. coli O157:H7 predictions obtained by using the U. S. Department of Agriculture Pathogen Modeling Program indicated that E. coli O104:H4 grows faster than E. coli O157:H7. The developed models were validated with alfalfa and broccoli sprouts. These models will provide risk assessors and food safety managers a rapid means of estimating the likelihood that the pathogen, if present, would grow in response to the interaction of the three variables assessed.

Validation of a Predictive Model for Survival and Growth of Salmonella Typhimurium DT104 on Chicken Skin for Extrapolation to a Previous History of Frozen Storage

The U.S. Department of Agriculture's tertiary Pathogen Modeling Program (PMP) model for survival and growth of Salmonella enterica ser. Typhimurium definitive type 104 (DT104) on chicken skin stored for 0 to 8 h at 5 to 50°C was evaluated for its ability to predict survival and growth of the same organism on chicken skin after frozen storage for 6 days at -20°C. Experimental design and methods used to collect data for model development (dependent data) were the same as those used to collect data for survival and growth after frozen storage (independent data for extrapolation). This was done to provide a valid comparison of observed and predicted values. The model was classified as providing acceptable predictions of the test data when the proportion of residuals in an acceptable prediction zone (pAPZ) from 21 log (fail-safe) to 0.5 log (fail-dangerous) was ≥0.7. The pAPZ for dependent data, independent data for interpolation, and independent data for extrapolation to a new independent variable of previous frozen storage were all acceptable (pAPZ ≥ 0.7), with the exception of the pAPZ for dependent data at 50°C, where an unacceptable pAPZ of 0.625 was obtained. Although a majority of observed log counts were less than predicted log counts, indicating that frozen storage of chicken skin for 6 days at -20°C had injured some Salmonella Typhimurium DT104, the injury was not large enough to cause the tertiary PMP model to provide unacceptable predictions. Thus, it was concluded that the tertiary PMP model provided valid predictions of survival and growth of Salmonella Typhimurium DT104 on chicken skin that had a previous history of frozen storage for 6 days at -20°C. Additional research is needed to determine how broadly the model can be applied to other conditions of previous frozen storage.

Development and Validation of a Mathematical Model for Growth of Pathogens in Cut Melons

Many outbreaks of foodborne illness associated with the consumption of fresh-cut melons have been reported. The objective of our research was to develop a mathematical model that predicts the growth rate of Salmonella on fresh-cut cantaloupe over a range of storage temperatures and to validate that model by using Salmonella and Escherichia coli O157:H7 on cantaloupe, honeydew, and watermelon, using both new data and data from the published studies. The growth of Salmonella on honeydew and watermelon and E. coli O157:H7 on cantaloupe, honeydew, and watermelon was monitored at temperatures of 4 to 25°C. The Ratkowsky (or square-root model) was used to describe Salmonella growth on cantaloupe as a function of storage temperature. Our results show that the levels of Salmonella on fresh-cut cantaloupe with an initial load of 3 log CFU/g can reach over 7 log CFU/g at 25°C within 24 h. No growth was observed at 4°C. A linear correlation was observed between the square root of Salmonella growth rate and temperature, such that √growth rate = 0.026 × (T - 5.613), R(2) = 0.9779. The model was generally suitable for predicting the growth of both Salmonella and E. coli O157:H7 on cantaloupe, honeydew, and watermelon, for both new data and data from the published literature. When compared with existing models for growth of Salmonella, the new model predicts a theoretic minimum growth temperature similar to the ComBase Predictive Models and Pathogen Modeling Program models but lower than other food-specific models. The ComBase Prediction Models results are very similar to the model developed in this study. Our research confirms that Salmonella can grow quickly and reach high concentrations when cut cantaloupe is stored at ambient temperatures, without visual signs of spoilage. Our model provides a fast and cost-effective method to estimate the effects of storage temperature on fresh-cut melon safety and could also be used in subsequent quantitative microbial risk assessments.

냉장돈육 가공공정 온도와 시간에서의 Wild-type Salmonella spp.의 성장특성 및 예측모델

본 연구에서는 멸균처리공정이 없는 돈육 포장공정을 대상으로 작업장에서 직접 분리한 야생균주인 Salmonella spp. KSC101를 작업장의 온도와 시간을 주요 변수로 하여, 이들 현장에서의 Salmonella spp. KSC101의 성장 특성을 파악하고, 이를 수학적으로 예측할 수 있는 모델을 개발하였다. 돈육포장공장 현장을 반영하여 온도는 0, 5, 10, 15, <TEX>$20^{\circ}C$</TEX>로, 시간은 0, 1, 2, 3시간으로 하였으며, <TEX>$0^{\circ}C$</TEX>와 <TEX>$5^{\circ}C$</TEX>에서는 성장이 발생하지 않았으며, <TEX>$10^{\circ}C$</TEX>, <TEX>$15^{\circ}C$</TEX>, <TEX>$20^{\circ}C$</TEX>에 는 약간의 성장이 있었으나 증가수준은 평균 0.34 log CFU/g정도였고, <TEX>$20^{\circ}C$</TEX>에서 성장율이 더 높았으나 <TEX>$15^{\circ}C$</TEX>와는 통계적으로는 유의하지 않았다(p < 0.05). 하지만 PMP와 비교시 야생균주인 Salmonella spp. KSC101의 성장이 더 빠른 것으로 나타났다. 이들 실험결과를 바탕으로 1차 모델은 Gompertz 4 parameter식을, 2차 모델은 Exponential decay식을 이용하여 성장예측모델을 개발하였으며, <TEX>$R^2$</TEX>값은 0.99이상으로 나타났다. 개발된 모델에 대한 검증으로 RMSE를 이용하였으며, 값이 0.103으로 양(+)의 방향으로 약간 초과 예측하는 것으로 나타났으나, 최종적으로 실험값과 예측값이 <TEX>${\pm}0.5$</TEX> log cfu/g 내에서 일치하고 있어, 본 연구에서 개발된 모델은 추후 냉장돈육 포장공정에서 위생관리기준 설정에 대한 과학적 근거자료로 활용할 수 있을 것이다. This study presents the influence on growth properties determined using a novel predictive growth model of wild-type Salmonella spp. KSC 101 by variations in the temperature and time during cut packaging in cold, uncooked pork meat. The experiment performed for model development included an arrangement of different temperatures (<TEX>$0^{\circ}C$</TEX>, <TEX>$5^{\circ}C$</TEX>, <TEX>$10^{\circ}C$</TEX>, <TEX>$15^{\circ}C$</TEX>, and <TEX>$20^{\circ}C$</TEX>) and time durations (0, 1, 2, and 3 hours) that reflect actual pork-cut and packaging processes. No growth was observed at <TEX>$0^{\circ}C$</TEX> and <TEX>$5^{\circ}C$</TEX>, whereas some growth was observed at <TEX>$10^{\circ}C$</TEX>, <TEX>$15^{\circ}C$</TEX>, and <TEX>$20^{\circ}C$</TEX>, with a mean increase of only 0.34 log CFU/g. The growth observed at <TEX>$20^{\circ}C$</TEX> was more robust than that observed at <TEX>$15^{\circ}C$</TEX>, but the difference was not statistically significant (p > 0.05). However, compared with PMP (Pathogen Modeling Program), the wild-type Salmonella spp. KSC 101 showed a more rapid growth. We used the Gompertz 4 parameter equation as the primary model, and the exponential decay formula as the secondary model. The estimated <TEX>$R^2$</TEX> values were 0.99 or higher. The developed model was evaluated by comparison of the experimental and predictive values, and the values were in agreement with the <TEX>${\pm}0.5$</TEX> log CFU/g, although the RMSE (Root mean square error) value was 0.103, which indicates a slight overestimation. Therefore, we suggest that the developed predictive growth model would be useful as a tool for evaluating sanitation criteria in pork cut-packaging processes.

Modeling the growth of Listeria monocytogenes in mold-ripened cheeses

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.

Dynamics of staphylococcal enterotoxin production in model experiments simulating the fresh cheese environment

The aim of this study was to evaluate the impact of internal factors (pH, NaCl) and external factors (temperature, incubation time) on the ability of Staphylococcus aureus to grow and to produce staphylococcal enterotoxins SEA, SEB, and SEC. The fresh cheese environment was modelled in Brain Heart Infusion Broth media and food matrices (pasteurized milk from retail outlets) by internal and external factors (pH = 4.5 and 5.5, 2% NaCl, and t = 8 °C and 15 °C). The counts of enterotoxigenic strains of S. aureus at baseline, i.e. at the time of inoculation of model samples, corresponded to those encountered in the production of fresh cheeses as a result of post-pasteurization contamination. Enumeration of S. aureus was performed in accordance with EN ISO 6888-1, using agar medium. Staphylococcal enterotoxins were detected by the enzyme-linked fluorescence assay. The pH (4.5) and refrigeration temperature (8 °C) used prevented S. aureus from achieving the critical count of 105 cfu·ml-1 specified in Commission Regulation (EC) No. 2073/2005. The highest rates of enterotoxin production were recorded for enterotoxin A. The growth curves of S. aureus derived from model experiments were compared with the growth curve generated by a predictive microbiology program - Pathogen Modelling Program. The results of this study proved the Pathogen Modelling Program to be suitable for use in the Hazard Analysis Critical Control Points system in the process of the fresh cheese production to help manufacturers prevent the growth of S. aureus and enterotoxin production.

Modeling the growth rate and lag time of different strains of Salmonella enterica and Listeria monocytogenes in ready-to-eat lettuce

The growth parameters (growth rate, μ and lag time, λ) of three different strains each of Salmonella enterica and Listeria monocytogenes in minimally processed lettuce (MPL) and their changes as a function of temperature were modeled. MPL were packed under modified atmosphere (5% O2, 15% CO2 and 80% N2), stored at 7–30 °C and samples collected at different time intervals were enumerated for S. enterica and L. monocytogenes. Growth curves and equations describing the relationship between μ and λ as a function of temperature were constructed using the DMFit Excel add-in and through linear regression, respectively. The predicted growth parameters for the pathogens observed in this study were compared to ComBase, Pathogen modeling program (PMP) and data from the literature. High R2 values (0.97 and 0.93) were observed for average growth curves of different strains of pathogens grown on MPL. Secondary models of μ and λ for both pathogens followed a linear trend with high R2 values (>0.90). Root mean square error (RMSE) showed that the models obtained are accurate and suitable for modeling the growth of S. enterica and L. monocytogenes in MP lettuce. The current study provides growth models for these foodborne pathogens that can be used in microbial risk assessment.