A probabilistic analysis of Clostridium perfringens growth during food service operations

A probabilistic approach to determine thermal process setting parameters: application for commercial sterility of products

The objective of the probabilistic data analysis presented in this paper was to enable the thermal process to be set on actual data rather than on generic or conservative rules. The application was an ambient stable soup product, heated in a continuous UHT line. The data set comes from a decade of microbiological analysis: initial spore load and survival spore concentration after moderate heat-treatment (100°C for 15 min and 110°C for 15 min) have been enumerated in forty eight ingredients. The probabilistic analysis was carried out within a risk-based context, considering a Performance Objective, PO, set after the heat-treatment process and an initial spore contamination (H₀) at the ingredient mixing step. The probabilistic analysis was based upon Bayesian inference, chosen for its flexibility when dealing with censored data (some values were reported as less than 1 log cfu/g) and also for its ability to incorporate in the data analysis prior information. Beforehand, Z values around 10°C for aerobic bacterial spores, and log count error around 1 log, were assumed. The methodology and the results are reported using two ingredients (garlic and milk powder) illustrating the 'not detected' (censored data) issue and also the inter-ingredient variability. Indeed, Z was estimated to be 13.6°C (mean) for spores selected from garlic and 5.9°C for those selected from milk powder. Based upon a hypothetical soup recipe with these two ingredients, the sterilization value was estimated to be 13 min (95th percentile). The potential use of similar methodology to design and set the sterilization value for the thermal process of future products, is discussed.

Extracting additional risk managers information from a risk assessment of Listeria monocytogenes in deli meats

The risk assessment study of Listeria monocytogenes in ready-to-eat foods conducted by the U.S. Food and Drug Administration is an example of an extensive quantitative microbiological risk assessment that could be used by risk analysts and other scientists to obtain information and by managers and stakeholders to make decisions on food safety management. The present study was conducted to investigate how detailed sensitivity analysis can be used by assessors to extract more information on risk factors and how results can be communicated to managers and stakeholders in an understandable way. The extended sensitivity analysis revealed that the extremes at the right side of the dose distribution (at consumption, 9 to 11.5 log CFU per serving) were responsible for most of the cases of listeriosis simulated. For concentration at retail, values below the detection limit of 0.04 CFU/g and the often used limit for L. monocytogenes of 100 CFU/g (also at retail) were associated with a high number of annual cases of listeriosis (about 29 and 82%, respectively). This association can be explained by growth of L. monocytogenes at both average and extreme values of temperature and time, indicating that a wide distribution can lead to high risk levels. Another finding is the importance of the maximal population density (i.e., the maximum concentration of L. monocytogenes assumed at a certain temperature) for accurately estimating the risk of infection by opportunistic pathogens such as L. monocytogenes. According to the obtained results, mainly concentrations corresponding to the highest maximal population densities caused risk in the simulation. However, sensitivity analysis applied to the uncertainty parameters revealed that prevalence at retail was the most important source of uncertainty in the model.

Predictive microbiology: Quantitative science delivering quantifiable benefits to the meat industry and other food industries

Predictive microbiology is considered in the context of the conference theme "chance, innovation and challenge", together with the impact of quantitative approaches on food microbiology, generally. The contents of four prominent texts on predictive microbiology are analysed and the major contributions of two meat microbiologists, Drs. T.A. Roberts and C.O. Gill, to the early development of predictive microbiology are highlighted. These provide a segue into R&D trends in predictive microbiology, including the Refrigeration Index, an example of science-based, outcome-focussed food safety regulation. Rapid advances in technologies and systems for application of predictive models are indicated and measures to judge the impact of predictive microbiology are suggested in terms of research outputs and outcomes. The penultimate section considers the future of predictive microbiology and advances that will become possible when data on population responses are combined with data derived from physiological and molecular studies in a systems biology approach. Whilst the emphasis is on science and technology for food safety management, it is suggested that decreases in foodborne illness will also arise from minimising human error by changing the food safety culture.

Information systems in food safety management

Information systems are concerned with data capture, storage, analysis and retrieval. In the context of food safety management they are vital to assist decision making in a short time frame, potentially allowing decisions to be made and practices to be actioned in real time. Databases with information on microorganisms pertinent to the identification of foodborne pathogens, response of microbial populations to the environment and characteristics of foods and processing conditions are the cornerstone of food safety management systems. Such databases find application in: Identifying pathogens in food at the genus or species level using applied systematics in automated ways. Identifying pathogens below the species level by molecular subtyping, an approach successfully applied in epidemiological investigations of foodborne disease and the basis for national surveillance programs. Predictive modelling software, such as the Pathogen Modeling Program and Growth Predictor (that took over the main functions of Food Micromodel) the raw data of which were combined as the genesis of an international web based searchable database (ComBase). Expert systems combining databases on microbial characteristics, food composition and processing information with the resulting "pattern match" indicating problems that may arise from changes in product formulation or processing conditions. Computer software packages to aid the practical application of HACCP and risk assessment and decision trees to bring logical sequences to establishing and modifying food safety management practices. In addition there are many other uses of information systems that benefit food safety more globally, including: Rapid dissemination of information on foodborne disease outbreaks via websites or list servers carrying commentary from many sources, including the press and interest groups, on the reasons for and consequences of foodborne disease incidents. Active surveillance networks allowing rapid dissemination of molecular subtyping information between public health agencies to detect foodborne outbreaks and limit the spread of human disease. Traceability of individual animals or crops from (or before) conception or germination to the consumer as an integral part of food supply chain management. Provision of high quality, online educational packages to food industry personnel otherwise precluded from access to such courses.

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.

Modeling growth of Clostridium perfringens in pea soup during cooling

Clostridium perfringens is a pathogen that mainly causes food poisoning outbreaks when large quantities of food are prepared. Therefore, a model was developed to predict the effect of different cooling procedures on the growth of this pathogen during cooling of food: Dutch pea soup. First, a growth rate model based on interpretable parameters was used to predict growth during linear cooling of pea soup. Second, a temperature model for cooling pea soup was constructed by fitting the model to experimental data published earlier. This cooling model was used to estimate the effect of various cooling environments on average cooling times, taking into account the effect of stirring and product volume. The growth model systematically overestimated growth of C. perfringens during cooling in air, but this effect was limited to less than 0.5 log N/ml and this was considered to be acceptable for practical purposes. It was demonstrated that the growth model for C. perfringens combined with the cooling model for pea soup could be used to sufficiently predict growth of C. perfringens in different volume sizes of pea soup during cooling in air as well as the effect of stirring, different cooling temperatures, and various cooling environments on the growth of C. perfringens in pea soup. Although fine-tuning may be needed to eliminate inaccuracies, it was concluded that the combined model could be a useful tool for designing good manufacturing practices (GMP) procedures.

Time–temperature profiles of chilled ready-to-eat foods in school catering and probabilistic analysis of Listeria monocytogenes growth

The purpose of this study was to evaluate the chill chain in school catering by monitoring time-temperature profiles. Chilled ready-to-eat foods have been chosen as subject of this study because of their high risk due to their production, storage and distribution steps, separated in time, followed by consumption without any further thermal treatment. In order to integrate the effects of storage duration and storage temperature, a quantitative criterion, namely "TTE" or "Time-Temperature Equivalent", was proposed. To illustrate the sanitary consequences of the recorded thermal history, Listeria monocytogenes growth was predicted based on reference growth curves in chilled ready-to-eat food products. The study of five centralised kitchens and 11 school-lunch canteens demonstrated in general a satisfactory maintenance of the chill chain. However, the coincidence of extended storage duration (due to weekends) and temperature abuse was observed and could lead to a significant microbial development.

Effect of cooling on Clostridium perfringens in pea soup

Foods associated with Clostridium perfringens outbreaks are usually abused after cooking. Because of their short generation times, C. perfringens spores and cells can grow out to high levels during improper cooling. Therefore, the potential of C. perfringens to multiply in Dutch pea soup during different cooling times was investigated. Tubes of preheated pea soup (50 degrees C) were inoculated with cocktails of cells or heat-activated spores of this pathogen. The tubes were linearly cooled to 15 degrees C in time spans of 3, 5, 7.5, and 10 h and were subsequently stored in a refrigerator at 3 or 7 degrees C for up to 84 h. Cell numbers increased by 1-log cycle during the 3-h cooling period and reached their maximum after 10 h of cooling. Subsequent refrigeration hardly reduced cell numbers. Cooling of 3.75 liters of pea soup in an open pan showed that this amount of pea soup cooled from 50 to 15 degrees C in 5 h, which will allow a more than 10-fold increase in cell numbers. These findings emphasize the need of good hygienic practices and quick cooling of heated foods after preparation.