Use of Modeling to Enhance the Microbiological Safety of the Food System

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.

Real-Time Bioluminescence Analysis of Escherichia coli O157:H7 Survival on Livestock Meats Stored Fresh, Cold, or Frozen

Foodborne bacteria such as Escherichia coli O157:H7 can cause severe hemorrhagic colitis in humans following consumption of contaminated meat products. Contamination with pathogenic bacteria is frequently found in the food production environment, and adequate household storage conditions of purchased foods are vital for illness avoidance. Real-time monitoring was used to evaluate bacterial growth in ground horse, beef, and pork meats maintained under various storage conditions. Various levels of E. coli O157:H7 carrying the luxCDABE operon, which allows the cells to emit bioluminescence, were used to inoculate meat samples that were then stored at room temperature for 0.5 day, at 4°C (cold) for 7 or 9 days, or -20°C (frozen) for 9 days. Real-time bioluminescence imaging (BLI) of bacterial growth was used to assess bacterial survival or load. Ground horse meat BLI signals and E. coli levels were dose and time dependent, increasing during room temperature and -20°C storage, but stayed at low levels during 4°C storage. No bacteria survived in the lower level inoculum groups (10¹ and 10³ CFU/g). With an inoculum of 10⁷ CFU/g, pork meats had higher BLI signals than did their beef counterparts, displaying decreased BLI signals during 7 days storage at 4°C. Both meat types had higher BLI signals in the fat area, which was confirmed with isolated fat tissues in the beef meat. Beef lean and fat tissues contrasted with both pork fat and lean tissues, which had significantly higher BLI signals and bacterial levels. BLI appears to be a useful research tool for real-time monitoring of bacterial growth and survival in various stored livestock meats. The dependence of E. coli O157:H7 growth on meat substrate (fat or lean) and storage conditions may be used as part of an effective antibacterial approach for the production of safe ground horse, beef, and pork meats.

Modelling the Growth Rate of Listeria Monocytogenes in Cooked Ham Stored at Different Temperatures

Introduction

The purpose of the study was to determine and model the growth rates of L. monocytogenes in cooked cured ham stored at various temperatures.

Material and Methods

Samples of cured ham were artificially contaminated with a mixture of three L. monocytogenes strains and stored at 3, 6, 9, 12, or 15ºC for 16 days. The number of listeriae was determined after 0, 1, 2, 3, 5, 7, 9, 12, 14, and 16 days. A series of decimal dilutions were prepared from each sample and plated onto ALOA agar, after which the plates were incubated at 37ºC for 48 h under aerobic conditions. The bacterial counts were logarithmised and analysed statistically. Five repetitions of the experiment were performed.

Results

Both storage temperature and time were found to significantly influence the growth rate of listeriae (P > 0.01). The test bacteria growth curves were fitted to three primary models: the Gompertz, Baranyi, and logistic. The mean square error (MSE) and Akaike's information criterion (AIC) were calculated to evaluate the goodness of fit. It transpired that the logistic model fit the experimental data best. The natural logarithms of L. monocytogenes' mean growth rates from this model were fitted to two secondary models: the square root and polynomial.

Conclusion

Modelling in both secondary types can predict the growth rates of L. monocytogenes in cooked cured ham stored at each studied temperature, but mathematical validation showed the polynomial model to be more accurate.

Modeling the effect of temperature on survival rate of Listeria monocytogenes in yogurt

The aim of the study was to (i) evaluate the behavior of Listeria monocytogenes in a commercially produced yogurt, (ii) determine the survival/inactivation rates of L. monocytogenes during cold storage of yogurt and (iii) to generate primary and secondary mathematical models to predict the behavior of these bacteria during storage at different temperatures. The samples of yogurt were inoculated with the mixture of three L. monocytogenes strains and stored at 3, 6, 9, 12 and 15°C for 16 days. The number of listeriae was determined after 0, 1, 2, 3, 5, 7, 9, 12, 14 and 16 days of storage. From each sample a series of decimal dilutions were prepared and plated onto ALOA agar (agar for Listeria according to Ottaviani and Agosti). It was found that applied temperature and storage time significantly influenced the survival rate of listeriae (p<0.01). The number of L. monocytogenes in all the samples decreased linearly with storage time. The slowest decrease in the number of the bacteria was found in the samples stored at 6°C (D-10 value = 243.9 h), whereas the highest reduction in the number of the bacteria was observed in the samples stored at 15°C (D-10 value = 87.0 h). The number of L. monocytogenes was correlated with the pH value of the samples (p<0.01). The natural logarithm of the mean survival/inactivation rates of L. monocytogenes calculated from the primary model was fitted to two secondary models, namely linear and polynomial. Mathematical equations obtained from both secondary models can be applied as a tool for the prediction of the survival/inactivation rate of L. monocytogenes in yogurt stored under temperature range from 3 to 15°C, however, the polynomial model gave a better fit to the experimental data.

Mathematical Modeling Tools to Study Preharvest Food Safety

This article provides an overview of the emerging field of mathematical modeling in preharvest food safety. We describe the steps involved in developing mathematical models, different types of models, and their multiple applications. The introduction to modeling is followed by several sections that introduce the most common modeling approaches used in preharvest systems. We finish the chapter by outlining potential future directions for the field.

Thermal tolerance characteristics of non-O157 Shiga toxigenic strains of Escherichia coli (STEC) in a beef broth model system are similar to those of O157:H7 STEC

The non-O157 Shiga toxigenic Escherichia coli (STEC) serogroups most commonly associated with illness are O26, O45, O103, O111, O121, and O145. In the United States, these serogroups are considered adulterants in raw nonintact beef. To begin to understand the behavior of these pathogens in meat systems, we compared the thermal tolerance of acid-adapted cells of non-O157 STEC and O157:H7 STEC in a beef-derived broth. D58°C-values were determined for at least three strains per serogroup, and D54.6°C-values and D63.6°C-values were determined for one strain per serogroup. Each strain was grown to stationary phase in brain heart infusion broth (BHIB; pH 7.0) and inoculated into prewarmed BHIB in a shaking water bath for thermotolerance experiments at 54.6, 58.0, or 63.6°C (three trials per strain). Samples were heated for up to 160 min at 54.6°C, 3 min at 58.0°C, or 45 s at 63.6°C, with periodic sampling followed by rapid cooling and plating on modified Levine's eosin methylene blue agar. For each strain and temperature, the log CFU per milliliter was plotted versus time, and D-values were determined. Across all strains, the least and most heat tolerant STEC serogroups at 58°C were O145 and O157, respectively. D58°C-values in BHIB ranged from 0.44 min for an O145 strain to 1.42 min for an O157:H7 strain. D58°C-values for O157 STEC strains were significantly higher than those for at least one strain in each of the non-O157 STEC serogroups (P < 0.05) except for serogroup O103. At 54.6°C, the most heat-resistant STEC strain belonged to serogroup O103 and was significantly more heat tolerant than the O157:H7 strains (P < 0.05). Grouping the strains, there were no significant differences in heat tolerance between O157 and non-O157 STEC at 63.6°C (P ≥ 0.05). The z-values for non-O157 STEC strains were comparable to those for O157:H7 STEC strains (P ≥ 0.05), ranging from 4.10 to 5.21°C. These results suggest that thermal processing interventions that target destruction of E. coli O157:H7 may have adequate lethality against non-O157 STEC.

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.

Implications of salt and sodium reduction on microbial food safety

Excess sodium consumption has been cited as a primary cause of hypertension and cardiovascular diseases. Salt (sodium chloride) is considered the main source of sodium in the human diet, and it is estimated that processed foods and restaurant foods contribute 80% of the daily intake of sodium in most of the Western world. However, ample research demonstrates the efficacy of sodium chloride against pathogenic and spoilage microorganisms in a variety of food systems. Notable examples of the utility and necessity of sodium chloride include the inhibition of growth and toxin production by Clostridium botulinum in processed meats and cheeses. Other sodium salts contributing to the overall sodium consumption are also very important in the prevention of spoilage and/or growth of microorganisms in foods. For example, sodium lactate and sodium diacetate are widely used in conjunction with sodium chloride to prevent the growth of Listeria monocytogenes and lactic acid bacteria in ready-to-eat meats. These and other examples underscore the necessity of sodium salts, particularly sodium chloride, for the production of safe, wholesome foods. Key literature on the antimicrobial properties of sodium chloride in foods is reviewed here to address the impact of salt and sodium reduction or replacement on microbiological food safety and quality.