Dynamic computer simulation of Clostridium perfringens growth in cooked ground beef

Thermal inactivation kinetics of Listeria monocytogenes in milk under isothermal and dynamic conditions

Thermal processing is a widely used method to ensure the microbiological safety of milk. Predictive microbiology plays a crucial role in quantifying microbial growth and decline, providing valuable guidance on the design and optimization of food processing operations. This study aimed to investigate the thermal inactivation kinetics of Listeria monocytogenes in milk under both isothermal and dynamic conditions. The thermal inactivation of L. monocytogenes was conducted under isothermal and non-isothermal conditions in sterilized and pasteurized milk, with and without background microbiota, respectively. Furthermore, a secondary model was developed between the shoulder effect and temperature, which was then integrated into the dynamic model. The results showed that L. monocytogenes grown in Tryptic Soy Yeast Extract Broth (TSBYE) prior to thermal inactivation exhibited higher heat resistance compared to cells grown in sterilized milk at isothermal temperatures of 60.0, 62.5, and 65℃. Moreover, the presence of background microbiota in milk significantly enhanced the heat resistance of L. monocytogenes, as evidenced by the increased D-values from 1.13 min to 2.34 min, from 0.46 min to 0.53 min, and from 0.25 min to 0.34 min at 60.0, 62.5, and 65 °C, respectively, regardless of whether the background microbiota was inactivated after co-growth or co-inactivated with L. monocytogenes. For non-isothermal inactivation, the one-step dynamic model based on the log-linear with shoulder model effectively described the microbial inactivation curve and exhibited satisfactory model performance. The model developed contributes to improved risk assessment, enabling dairy processors to optimize thermal treatment and ensure microbiological safety.

A Theoretical Analysis for Assessing the Variability of Secondary Model Thermal Inactivation Kinetic Parameters

Traditionally, for the determination of the kinetic parameters of thermal inactivation of a heat labile substance, an appropriate index is selected and its change is measured over time at a series of constant temperatures. The rate of this change is described through an appropriate primary model and a secondary model is applied to assess the impact of temperature. By this approach, the confidence intervals of the estimates of the rate constants are not taken into account. Consequently, the calculated variability of the secondary model parameters can be significantly lower than the actual variability. The aim of this study was to demonstrate the influence of the variability of the primary model parameters in establishing the confidence intervals of the secondary model parameters. Using a Monte Carlo technique and assuming normally distributed DT values (parameter associated with a primary inactivation model), the error propagating on the DTref and z-values (secondary model parameters) was assessed. When DT confidence intervals were broad, the secondary model's parameter variability was appreciably high and could not be adequately estimated through the traditional deterministic approach that does not take into account the variation on the DT values. In such cases, the proposed methodology was essential for realistic estimations.

Lactic acid bacteria as protective cultures in fermented pork meat to prevent Clostridium spp. growth

In meat fermented foods, Clostridium spp. growth is kept under control by the addition of nitrite. The growing request of consumers for safer products has led to consider alternative bio-based approaches, the use of protective cultures being one of them. This work is aimed at checking the possibility of using two Lactobacillus spp. strains as protective cultures against Clostridium spp. in pork ground meat for fermented salami preparation. Both Lactobacillus strains displayed anti-clostridia activity in vitro using the spot agar test and after co-culturing them in liquid medium with each Clostridium strain. Only one of them, however, namely L. plantarum PCS20, was capable of effectively surviving in ground meat and of performing anti-microbial activity in carnis in a challenge test where meat was inoculated with the Clostridium strain. Therefore, this work pointed out that protective cultures can be a feasible approach for nitrite reduction in fermented meat products.

Differential outgrowth potential of Clostridium perfringens food-borne isolates with various cpe-genotypes in vacuum-packed ground beef during storage at 12°C

In the current study, the outgrowth of spores of 15 different food isolates of Clostridium perfringens was evaluated in vacuum-packed ground beef during storage at 12°C and 25°C. This included enterotoxic strains carrying the gene encoding the CPE enterotoxin on the chromosome (C-cpe), on a plasmid (P-cpe) and cpe-negative strains. The 15 strains were selected from a larger group of strains that were first evaluated for their ability to sporulate in modified Duncan-Strong sporulating medium. Sporulation ability varied greatly between strains but was not associated with a particular cpe genotype. In line with previous studies, the tested C-cpe strains produced spores with significantly higher heat resistance than the cpe-negative and P-cpe strains (both IS1151 and IS1470-like) with the exception of strain VWA009. Following inoculation of vacuum-packed cooked ground beef with spores, the heat-resistant C-cpe strains showed lower outgrowth potential in this model food stored at 12°C than the P-cpe and cpe-negative strains, while no significant differences were observed at 25°C. These results suggest that the latter strains may have a competitive advantage over C-cpe strains at reduced temperatures during storage of foods that support the growth of C. perfringens. While spores of P-cpe strains are readily inactivated by heat processing, post-processing contamination by food handlers who may carry P-cpe strains that have a better growth potential at lower temperatures must be avoided. The varying responses of C. perfringens spores to heat and the differences in outgrowth capacity at different temperatures are factors to be considered in strain selection for challenge tests, and for predictive modelling of C. perfringens.

MicroFit: um software gratuito para desenvolvimento e ajuste de modelos matemáticos de crescimento bacteriano

A conservação de alimentos é uma das práticas mais antigas apropriadas pela humanidade, entretanto as infecções alimentares ainda despontam como uma gravíssima questão de saúde pública em todo o mundo, levando a óbito milhares de pessoas todos os anos. Nesse contexto, os modelos de predição microbiológica surgem como uma importante ferramenta para aferição da segurança alimentar e avaliação do risco de contaminação de alimentos. Os parâmetros de predição do comportamento microbiano proporcionam um caminho rápido e relativamente econômico para obtenção de estimativas confiáveis sobre crescimento, inativação e sobrevivência durante a atividade de micro-organismos nos alimentos, de acordo com as condições de estocagem. Apesar da importância dos modelos de predição, as dificuldades de utilização e resolução dos mesmos, por consistirem de equações não lineares e sem solução analítica, impedem sua ampla utilização. A baixa disponibilidade de programas eletrônicos para análise de dados microbiológicos, bem como a falta de ferramentas que permitam a criação de modelos ou o ajuste de parâmetros de uma forma rápida e fácil são outros entraves ao uso dos modelos de crescimento microbiológico. Diante desse problema, esse artigo tem por objetivo descrever o processo de desenvolvimento de um software concebido para permitir o ajuste, criação e comparação de modelos microbiológicos de crescimento de bactérias. A partir do uso de ferramentas comumente empregadas nesse tipo de estudo e das necessidades não contempladas por elas, foi desenvolvido um programa autônomo, gratuito, dotado de uma interface objetiva e com mecanismos capazes de criar e avaliar facilmente modelos preditivos. Espera-se que o software desenvolvido neste trabalho possa facilitar a análise de dados do comportamento microbiano em alimentos e o controle de possíveis repercussões para o consumidor final.

Dynamic predictive model for the growth of Salmonella spp. in liquid whole egg

A dynamic model for the growth of Salmonella spp. in liquid whole egg (LWE) (approximately pH 7.8) under continuously varying temperature was developed. The model was validated using 2 (5 to 15 °C; 600 h and 10 to 40 °C; 52 h) sinusoidal, continuously varying temperature profiles. LWE adjusted to pH 7.8 was inoculated with approximately 2.5-3.0 log CFU/mL of Salmonella spp., and the growth data at several isothermal conditions (5, 7, 10, 15, 20, 25, 30, 35, 37, 39, 41, 43, 45, and 47 °C) was collected. A primary model (Baranyi model) was fitted for each temperature growth data and corresponding maximum growth rates were estimated. Pseudo-R2 values were greater than 0.97 for primary models. Modified Ratkowsky model was used to fit the secondary model. The pseudo-R2 and root mean square error were 0.99 and 0.06 log CFU/mL, respectively, for the secondary model. A dynamic model for the prediction of Salmonella spp. growth under varying temperature conditions was developed using 4th-order Runge-Kutta method. The developed dynamic model was validated for 2 sinusoidal temperature profiles, 5 to 15 °C (for 600 h) and 10 to 40 °C (for 52 h) with corresponding root mean squared error values of 0.28 and 0.23 log CFU/mL, respectively, between predicted and observed Salmonella spp. populations. The developed dynamic model can be used to predict the growth of Salmonella spp. in LWE under varying temperature conditions.

PRACTICAL APPLICATION

  Liquid egg and egg products are widely used in food processing and in restaurant operations. These products can be contaminated with Salmonella spp. during breaking and other unit operations during processing. The raw, liquid egg products are stored under refrigeration prior to pasteurization. However, process deviations can occur such as refrigeration failure, leading to temperature fluctuations above the required temperatures as specified in the critical limits within hazard analysis and critical control point plans for the operations. The processors are required to evaluate the potential growth of Salmonella spp. in such products before the product can be used, or further processed. Dynamic predictive models are excellent tools for regulators as well as the processing plant personnel to evaluate the microbiological safety of the product under such conditions.

Risk assessment for Clostridium perfringens in ready-to-eat and partially cooked meat and poultry products

An assessment of the risk of illness associated with Clostridium perfringens in ready-to-eat and partially cooked meat and poultry products was completed to estimate the effect on the annual frequency of illnesses of changing the allowed maximal 1-log growth of C. perfringens during stabilization (cooling after the manufacturing heat step). The exposure assessment modeled stabilization, storage, and consumer preparation such as reheating and hot-holding. The model predicted that assuming a 10- or 100-fold increase from the assumed 1-log (maximal allowable) growth of C. perfringens results in a 1.2- or 1.6-fold increase of C. perfringens-caused illnesses, respectively, at the median of the uncertainty distribution. Improper retail and consumer refrigeration accounted for approximately 90% of the 79,000 C. perfringens illnesses predicted by the model at 1-log growth during stabilization. Improper hot-holding accounted for 8% of predicted illnesses, although model limitations imply that this is an underestimate. Stabilization accounted for less than 1% of illnesses. Efforts to reduce illnesses from C. perfringens in ready-to-eat and partially cooked meat and poultry products should focus on retail and consumer storage and preparation methods.

Modeling the effect of temperature on growth of Salmonella in chicken

Growth data of Salmonella in chicken were collected at several isothermal conditions (10, 15, 20, 25, 28, 32, 35, 37, 42, and 45 degrees C) and were then fitted into primary models, namely the logistic model, modified Gompertz model and Baranyi model. Measures of goodness-of-fit such as mean square error, pseudo-R(2), -2 log likelihood, Akaike's information, and Sawa's Bayesian information criteria were used for comparison for these primary models. Based on these criteria, modified Gompertz model described growth data the best, followed by the Baranyi model, and then the logistic model. The maximum growth rates obtained from each primary model were then modeled as a function of temperature using the modified Ratkowsky model. Pseudo-R(2) values for this secondary model describing growth rate obtained from Baranyi, modified Gompertz, and logistic models were 0.999, 0.980, and 0.990, respectively. Mean square error values for corresponding models were 0.0002, 0.0008, and 0.0009, respectively. Both measures clearly show that the Baranyi model performed better than the modified Gompertz model or the logistic model.

Predictive model for Clostridium perfringens growth in roast beef during cooling and inhibition of spore germination and outgrowth by organic acid salts

Spores of foodborne pathogens can survive traditional thermal processing schedules used in the manufacturing of processed meat products. Heat-activated spores can germinate and grow to hazardous levels when these products are improperly chilled. Germination and outgrowth of Clostridium perfringens spores in roast beef during chilling was studied following simulated cooling schedules normally used in the processed-meat industry. Inhibitory effects of organic acid salts on germination and outgrowth of C. perfringens spores during chilling and the survival of vegetative cells and spores under abusive refrigerated storage was also evaluated. Beef top rounds were formulated to contain a marinade (finished product concentrations: 1% salt, 0.2% potassium tetrapyrophosphate, and 0.2% starch) and then ground and mixed with antimicrobials (sodium lactate and sodium lactate plus 2.5% sodium diacetate and buffered sodium citrate and buffered sodium citrate plus 1.3% sodium diacetate). The ground product was inoculated with a three-strain cocktail of C. perfringens spores (NCTC 8238, NCTC 8239, and ATCC 10388), mixed, vacuum packaged, heat shocked for 20 min at 75 degrees C, and chilled exponentially from 54.5 to 7.2 degrees C in 9, 12, 15, 18, or 21 h. C. perfringens populations (total and spore) were enumerated after heat shock, during chilling, and during storage for up to 60 days at 10 degrees C using tryptose-sulfite-cycloserine agar. C. perfringens spores were able to germinate and grow in roast beef (control, without any antimicrobials) from an initial population of ca. 3.1 log CFU/g by 2.00, 3.44, 4.04, 4.86, and 5.72 log CFU/g after 9, 12, 15, 18, and 21 h of exponential chilling. A predictive model was developed to describe sigmoidal C. perfringens growth curves during cooling of roast beef from 54.5 to 7.2 degrees C within 9, 12, 15, 18, and 21 h. Addition of antimicrobials prevented germination and outgrowth of C. perfringens regardless of the chill times. C. perfringens spores could be recovered from samples containing organic acid salts that were stored up to 60 days at 10 degrees C. Extension of chilling time to > or =9 h resulted in >1 log CFU/g growth of C. perfringens under anaerobic conditions in roast beef. Organic acid salts inhibited outgrowth of C. perfringens spores during chilling of roast beef when extended chill rates were followed. Although C. perfringens spore germination is inhibited by the antimicrobials, this inhibition may represent a hazard when such products are incorporated into new products, such as soups and chili, that do not contain these antimicrobials, thus allowing spore germination and outgrowth under conditions of temperature abuse.

Stochastically modeling Listeria monocytogenes growth in farm tank milk

This article presents a Listeria monocytogenes growth model in milk at the farm bulk tank stage. The main objective was to judge the feasibility and value to risk assessors of introducing a complex model, including a complete thermal model, within a microbial quantitative risk assessment scheme. Predictive microbiology models are used under varying temperature conditions to predict bacterial growth. Input distributions are estimated based on data in the literature, when it is available. If not, reasonable assumptions are made for the considered context. Previously published results based on a Bayesian analysis of growth parameters are used. A Monte Carlo simulation that forecasts bacterial growth is the focus of this study. Three scenarios that take account of the variability and uncertainty of growth parameters are compared. The effect of a sophisticated thermal model taking account of continuous variations in milk temperature was tested by comparison with a simplified model where milk temperature was considered as constant. Limited multiplication of bacteria within the farm bulk tank was modeled. The two principal factors influencing bacterial growth were found to be tank thermostat regulation and bacterial population growth parameters. The dilution phenomenon due to the introduction of new milk was the main factor affecting the final bacterial concentration. The results show that a model that assumes constant environmental conditions at an average temperature should be acceptable for this process. This work may constitute a first step toward exposure assessment for L. monocytogenes in milk. In addition, this partly conceptual work provides guidelines for other risk assessments where continuous variation of a parameter needs to be taken into account.

Development of an integrated model for heat transfer and dynamic growth of Clostridium perfringens during the cooling of cooked boneless ham

Numerous small meat processors in the United States have difficulties complying with the stabilization performance standards for preventing growth of Clostridium perfringens by 1 log10 cycle during cooling of ready-to-eat (RTE) products. These standards were established by the Food Safety and Inspection Service (FSIS) of the US Department of Agriculture in 1999. In recent years, several attempts have been made to develop predictive models for growth of C. perfringens within the range of cooling temperatures included in the FSIS standards. Those studies mainly focused on microbiological aspects, using hypothesized cooling rates. Conversely, studies dealing with heat transfer models to predict cooling rates in meat products do not address microbial growth. Integration of heat transfer relationships with C. perfringens growth relationships during cooling of meat products has been very limited. Therefore, a computer simulation scheme was developed to analyze heat transfer phenomena and temperature-dependent C. perfringens growth during cooling of cooked boneless cured ham. The temperature history of ham was predicted using a finite element heat diffusion model. Validation of heat transfer predictions used experimental data collected in commercial meat-processing facilities. For C. perfringens growth, a dynamic model was developed using Baranyi's nonautonomous differential equation. The bacterium's growth model was integrated into the computer program using predicted temperature histories as input values. For cooling cooked hams from 66.6 degrees C to 4.4 degrees C using forced air, the maximum deviation between predicted and experimental core temperature data was 2.54 degrees C. Predicted C. perfringens growth curves obtained from dynamic modeling showed good agreement with validated results for three different cooling scenarios. Mean absolute values of relative errors were below 6%, and deviations between predicted and experimental cell counts were within 0.37 log10 CFU/g. For a cooling process which was in exact compliance with the FSIS stabilization performance standards, a mean net growth of 1.37 log10 CFU/g was predicted. This study introduced the combination of engineering modeling and microbiological modeling as a useful quantitative tool for general food safety applications, such as risk assessment and hazard analysis and critical control points (HACCP) plans.

Improvement of new logistic model for bacterial growth

Recently Fujikawa et al. [J. Food Hyg. Soc. Japan, 44, 155-160 (2003)] developed a new logistic model for bacterial growth. In the present study, an adjustment factor in the model was improved. The improved model could successfully describe growth curves of Escherichia coli and Salmonella in liquid media. In particular, the model could describe the linear growth at the early logarithmic phase more accurately than the previous model, being similar in this respect to the Baranyi model. However, the improved model more accurately predicted the rate constant of growth and the duration of the lag time as compared with the Baranyi model. These results showed that the improved model has the potential to successfully predict microbial growth.

Growth potential of Clostridium perfringens during cooling of cooked meats

Many meat-based food products are cooked to temperatures sufficient to inactivate vegetative cells of Clostridium perfringens, but spores of this bacterium can survive, germinate, and grow in these products if sufficient time, temperature, and other variables exist. Because ingestion of large numbers of vegetative cells can lead to concomitant sporulation, enterotoxin release in the gastrointestinal tract, and diarrhea-like illness, a necessary food safety objective is to ensure that not more than acceptable levels of C. perfringens are in finished products. As cooked meat items cool they will pass through the growth temperature range of C. perfringens (50 to 15 degrees C). Therefore, an important step in determining the likely level of C. perfringens in the final product is the estimation of growth of the pathogen during cooling of the cooked product. Numerous studies exist dealing with just such estimations, yet consensual methodologies, results, and conclusions are lacking. There is a need to consider the bulk of C. perfringens work relating to cooling of cooked meat-based products and attempt to move toward a better understanding of the true growth potential of the organism. This review attempts to summarize observations made by researchers and highlight variations in experimental approach as possible explanations for different outcomes. An attempt is also made here to identify and justify optimal procedures for conducting C. perfringens growth estimation in meat-based cooked food products during cooling.