T Ross

Growth Limits of Listeria monocytogenes as a Function of Temperature, pH, NaCl, and Lactic Acid

ABSTRACT Models describing the limits of growth of pathogens under multiple constraints will aid management of the safety of foods which are sporadically contaminated with pathogens and for which subsequent growth of the pathogen would significantly increase the risk of food-borne illness. We modeled the effects of temperature, water activity, pH, and lactic acid levels on the growth of two strains ofListeria monocytogenes in tryptone soya yeast extract broth. The results could be divided unambiguously into “growth is possible” or “growth is not possible” classes. We observed minor differences in growth characteristics of the two L. monocytogenes strains. The data follow a binomial probability distribution and may be modeled using logistic regression. The model used is derived from a growth rate model in a manner similar to that described in a previously published work (K. A. Presser, T. Ross, and D. A. Ratkowsky, Appl. Environ. Microbiol. 64:1773–1779, 1998). We used “nonlinear logistic regression” to estimate the model parameters and developed a relatively simple model that describes our experimental data well. The fitted equations also described well the growth limits of all strains of L. monocytogenesreported in the literature, except at temperatures beyond the limits of the experimental data used to develop the model (3 to 35°C). The models developed will improve the rigor of microbial food safety risk assessment and provide quantitative data in a concise form for the development of safer food products and processes.

Estimation of bacterial growth rates from turbidimetric and viable count data

The relationship between maximum specific growth rates (mu max) determined from viable counts and turbidimetric measurements for a range of bacterial species is examined in order to assess the potential of turbidimetric methods in predictive microbiology. Two methods for the estimation of mu max from turbidimetric data are presented. One is based on absorbance and the other on transmittance measurements. Both are compared to estimates obtained by viable count methods. Calibration factors, a function to correct the non-linearity of absorbance measurements, and variance stabilising transformations for corrected absorbance measurements and for viable count data, are determined. It is concluded that turbidimetric measurements may be used reliably for estimation of mu max.

Modelling the bacterial growth/no growth interface

A logistic regression model is proposed which enables one to model the boundary between growth and no growth for bacterial strains in the presence of one or more growth controlling factors such as temperature, pH and additives such as salt and sodium nitrite. The form of the expression containing the growth limiting factors may be suggested by a kinetic model, while the response at a given combination of factors may either be presence/absence (i.e. growth/no growth) or probabilistic (i.e. r successes in n trials). The approach described represents an integration of the probability and kinetic aspects of predictive microbiology, and a unification of predictive microbiology and the hurdle concept. The model is illustrated using data for Shigella flexneri.

A simple, spreadsheet-based, food safety risk assessment tool

The development and use of a simple tool for food safety risk assessment is described. The tool is in spreadsheet software format and embodies established principles of food safety risk assessment, i.e., the combination of probability of exposure to a food-borne hazard, the magnitude of hazard in a food when present, and the probability and severity of outcomes that might arise from that level and frequency of exposure. The tool requires the user to select from qualitative statements and/or to provide quantitative data concerning factors that that will affect the food safety risk to a specific population, arising from a specific food product and specific hazard, during the steps from harvest to consumption. The spreadsheet converts the qualitative inputs into numerical values and combines them with the quantitative inputs in a series of mathematical and logical steps using standard spreadsheet functions. Those calculations are used to generate indices of the public health risk. Shortcomings of the approach are discussed, including the simplifications and assumptions inherent in the mathematical model, the inadequacy of data currently available, and the lack of consideration of variability and uncertainty in the inputs and outputs of the model. Possible improvements are suggested. The model underpinning the tool is a simplification of the harvest to consumption pathway, but the tool offers a quick and simple means of comparing food-borne risks from diverse products, and has utility for ranking and prioritising risks from diverse sources. It can be used to screen food-borne risks and identify those requiring more rigorous assessment. It also serves as an aid to structured problem solving and can help to focus attention on those factors in food production, processing, distribution and meal preparation that most affect food safety risk, and that may be the most appropriate targets for risk management strategies.

Modelling the effects of temperature, water activity, pH and lactic acid concentration on the growth rate of Escherichia coli

An extended square root-type model describing Escherichia coli growth rate was developed as a function of temperature (7.63-47.43 degrees C), water activity (0.951-0.999, adjusted with NaCl), pH (4.02-8.28) and lactic acid concentration (0-500 mM). The new model, based on 236 growth rate data, combines and extends previously published square root-type models and incorporates terms for upper and lower limiting temperatures, upper and lower limiting pH, minimum inhibitory concentrations of dissociated and undissociated lactic acid and lower limiting water activity. A term to describe upper limiting water activity was developed but could not be fitted to the E. coli data set because of the difficulty of generating data in the super-optimal water activity range (i.e. >0.998). All data used to generate the model are presented. The model provides an excellent description of the experimental data.

Development and evaluation of a predictive model for the effect of temperature and water activity on the growth rate of Vibrio parahaemolyticus

The growth rates of four strains of Vibrio parahaemolyticus were measured and compared in a model broth system. The results for the fastest growing strain, based on 77 combinations of temperature and water activity (aw) using NaCl as the humectant, were summarised in the form of a predictive mathematical model. The model, of the square-root type includes a novel term to describe the effects of super-optimal water activity, and can be used to predict generation times for the temperature range (8-45 degrees C) and water activity range (0.936-0.995) which permit growth of Vibrio parahaemolyticus. Predicted generation times from the model were compared to literature data, using bias and accuracy factors, for both laboratory media and foods. The model was shown to give realistic growth estimates, with a bias value of 1.01, and an accuracy factor of 1.38.

Development of a predictive model to describe the effects of temperature and water activity on the growth of spoilage pseudomonads

A combined temperature and water activity model for the growth of psychrotrophic pseudomonads was developed using turbidimetric data. Psychrotrophic pseudomonads were isolated from various modified and whole milks. The fastest growing strain was identified and used to develop the model. Generation time estimates calculated by turbidimetric and viable count data differed but this difference was constant with respect to temperature and was incorporated into the modelling process so that all models are constructed to predict generation times equivalent to those calculated by viable counts, the standard method for enumerating microorganisms in food products.

Variability in biofilm production by Listeria monocytogenes correlated to strain origin and growth conditions

This study aimed to identify factors that influence the development of biofilm by Listeria monocytogenes strains and to determine the extent to which biofilm production protects against quaternary ammonium compound (QAC) disinfectant challenge. A total of 95 L. monocytogenes strains were studied and biofilm production was assessed as a function of incubation temperature, media pH, strain origin, serotype, and environmental persistence status. Attachment and biofilm development (inferred by the level of attached biomass) were measured in vitro using a colourimetric 96-well microtitre plate method in nutritive media (Brain-Heart Infusion). Increased biofilm production correlated with increasing temperature and the most acidic, or most alkaline, growth conditions tested. Clinical and environmental (food factory) strains were observed to increase biofilm production at higher and lower incubation temperatures respectively, independent of their rate of planktonic growth. Serotype 1/2a strains produced significantly more biofilm. Biofilm maturity, rather than strain, was correlated with resistance to QAC. Carbohydrate containing exopolymeric material could not be detected in the biofilm of representative strains, and no correlation between strains recovered as persistent food factory contaminants and biofilm production was identified. Although limited to in vitro inference based on the assay system used, our results suggest that environmental conditions determine the level of biofilm production by L. monocytogenes strains, independent of the rate of planktonic growth, and that this may manifest from selection pressures to which a given strain grows optimally.

Use of bacteriophages as biocontrol agents to control Salmonella associated with seed sprouts

Two Salmonella bacteriophages (SSP5 and SSP6) were isolated and characterized based on their morphology and host range, and evaluated for their potential to control Salmonella Oranienburg in vitro and on experimentally contaminated alfalfa seeds. Phages SSP5 and SSP6 were classified as members of the Myoviridae and Siphoviridae families, respectively. Both phages had a broad host range of over 65% of the 41 Salmonella strains tested. During in vitro trials, the phages resulted in incomplete lysis of Salmonella cultures, in spite of high levels of phage remaining in the system. Phage SSP5 was more effective in reducing Salmonella populations. Addition of phage SSP6 to alfalfa seeds previously contaminated with S. Oranienburg caused an approximately 1 log(10) CFU g(-1) reduction of viable Salmonella, which was achieved 3 h after phage application. Thereafter the phage had no inhibitory effect on Salmonella population growth. A second addition of the same (SSP6) or different (SSP5) phage to a Salmonella culture treated with phage SSP6, did not affect Salmonella populations. It was further shown that development of Salmonella permanently resistant to phage was not evident in either seed or in vitro challenge trials, suggesting the existence of a temporary, acquired, non-specific phage resistance phenomenon. These factors may complicate the use of phages for biocontrol.

A semi-quantitative seafood safety risk assessment

As part of a semi-quantitative risk assessment of 10 seafood hazard/product combinations, a risk assessment tool was used to generate a Risk Ranking. The tool is in a spreadsheet software format and provides a risk estimate, which is scaled between 0 and 100, where 0 represents no risk and 100 represents all meals containing a lethal dose of the hazard. A full description of the tool is contained in Ross and Sumner (this issue). Based on their ranking, seafoods in Australia fell into three risk categories. Hazard/product pairs with ranking < 32 included mercury poisoning (Relative Risk = 24), Clostridium botulinum in canned fish (RR = 25), or in vacuum-packed cold-smoked fish (RR = 28), parasites in sushi/sashimi (RR = 31), viruses in shellfish from uncontaminated waters, (RR = 31), enteric bacteria in imported cooked shrimp (RR = 31) and algal biotoxins from controlled waters (RR = 31). It is noted that there have been no documented cases of food-borne illness from any of the above hazard/product pairings in Australia. Those with rankings 32-48 included Vibrio parahaemolyticus in cooked prawns (RR = 37), V. cholerae in cooked prawns (RR = 37), Listeria monocytogenes in cold-smoked seafoods (RR = 39), scombrotoxicosis (RR = 40), V. vulnificus in oysters (RR = 41), ciguatera in the general Australian population (RR = 45), L. monocytogenes in susceptible (RR = 45) and extremely susceptible populations (RR = 47) and enteric bacteria in imported cooked shrimp eaten by vulnerable consumers (RR = 48). Almost all the hazard/product pairs in this category have caused the outbreaks of food poisoning in Australasia. Those hazard/product pairs with rankings >48 included ciguatera from recreational fishing in susceptible areas (RR = 60), viruses in shellfish from contaminated waters (RR = 67) and algal biotoxins from uncontrolled waters in an algal event (RR = 72). There have been significant (>100 cases) food poisoning incidents involving viruses and biotoxins in shellfish, while ciguatera poisoning is prevalent among coastal communities in Australias warmer waters.