Simultaneous construction of PCR-DGGE-based predictive models of Listeria monocytogenes and Vibrio parahaemolyticus on cooked shrimps

The aim of this study was to simultaneously construct PCR-DGGE-based predictive models of Listeria monocytogenes and Vibrio parahaemolyticus on cooked shrimps at 4 and 10°C. Calibration curves were established to correlate peak density of DGGE bands with microbial counts. Microbial counts derived from PCR-DGGE and plate methods were fitted by Baranyi model to obtain molecular and traditional predictive models. For L. monocytogenes, growing at 4 and 10°C, molecular predictive models were constructed. It showed good evaluations of correlation coefficients (R(2) > 0.92), bias factors (Bf ) and accuracy factors (Af ) (1.0 ≤ Bf ≤ Af ≤ 1.1). Moreover, no significant difference was found between molecular and traditional predictive models when analysed on lag phase (λ), maximum growth rate (μmax ) and growth data (P > 0.05). But for V. parahaemolyticus, inactivated at 4 and 10°C, molecular models show significant difference when compared with traditional models. Taken together, these results suggest that PCR-DGGE based on DNA can be used to construct growth models, but it is inappropriate for inactivation models yet. This is the first report of developing PCR-DGGE to simultaneously construct multiple molecular models.

SIGNIFICANCE AND IMPACT OF THE STUDY

It has been known for a long time that microbial predictive models based on traditional plate methods are time-consuming and labour-intensive. Denaturing gradient gel electrophoresis (DGGE) has been widely used as a semiquantitative method to describe complex microbial community. In our study, we developed DGGE to quantify bacterial counts and simultaneously established two molecular predictive models to describe the growth and survival of two bacteria (Listeria monocytogenes and Vibrio parahaemolyticus) at 4 and 10°C. We demonstrated that PCR-DGGE could be used to construct growth models. This work provides a new approach to construct molecular predictive models and thereby facilitates predictive microbiology and QMRA (Quantitative Microbial Risk Assessment).

Can a participatory approach contribute to food chain risk analysis?

We consider food chain risks and specifically address stakeholder participation in the risk analysis process. We combine social and natural science perspectives to explore the participation process in relation to food risks and, in particular, to consider how some specific participation processes might be scientifically evaluated and how stakeholder participation in general might be incorporated into food risk decision making. We have built considerations based on three large integrative case studies that examine aspects of participatory processes. Here we use the case studies collectively to illustrate observations and beliefs concerning the nature of the interaction of stakeholders with established quantitative risk methodologies. This account is not supported by any large volume of analysis. The views in the report are expressed in relation to an accepted risk analysis framework and also with respect to probabilistic modeling of risks and are illustrated where possible with anecdotal reports of actual case study events.

Probabilistic representation of the exposure of consumers to neurotoxin in a minimally processed potato product

We have examined the potential of a well-specified, minimally processed potato product as a vehicle for the exposure of consumers to Clostridium botulinum neurotoxin. The product is a relatively simple combination of raw potato flakes, flour, starch and other minor ingredients and has an extended lifetime under refrigeration conditions. A combination of information and data, from a variety of sources that includes the manufacturer, has shown that the product is particularly safe with respect to non-proteolytic C. botulinum hazards. The model concentrates on a simple end point, the toxicity of an individual retail unit of the product at the point of consumer preparation, which is related to an individual risk. The probabilistic analysis was built using Bayesian Belief Network (BBN) techniques.

Germination and growth from spores: variability and uncertainty in the assessment of food borne hazards

We have developed a model for the variability of spore lag times and shown that variability has an important role in the quantitative assessment of risks associated with spore forming bacteria in food. The model includes two sequential independent delay times that contribute to the lag time for a single spore. We have shown that a population of variable spores also has a variable lag time, and we have emphasised the significance of this variability in quantitative representations of population dynamics for small populations. We have made a Bayesian estimate for the extent of the variability in spore lag times and made a comparison with direct microscopic observations of individual spores of nonproteolytic Clostridium botulinum. We conclude that Bayesian inference is a practical method for quantifying variability and hence a significant element in the development of quantitative risk assessments for hazards associated with spore forming bacteria.

Relevance of microbial interactions to predictive microbiology

Microbial interaction can be ignored in predictive microbiology under most conditions. We show that interactions are only important at high population densities, using published data on inhibition of growth of Listeria monocytogenes in broth. Our analysis using growth models from predictive microbiology indicated that interactions only occur at population densities of approximately 10(8) cfu/ml of the protective cultures. Spoilage is evident at these levels, except for fermented foods. In bacterial colonies, diffusion limitation acts as a constraint to growth. We have shown that these constraints only become important after large outgrowth of colonies (in the order of 5-log growth in Lactobacillus curvatus colonies), which depends on the initial inoculation density. Intra-colony interactions play an important role under these conditions. There is no large outgrowth of colonies when the initial inoculation densities are high and broth culture growth can be used to approximate colony growth.

Microgradients in bacterial colonies: use of fluorescence ratio imaging, a non-invasive technique

Fluorescence ratio imaging is a non-invasive technique for studying the formation of microgradients in immobilised bacterial colonies. These gradients can be quantified easily when combined with the gel cassette system designed at the Institute of Food Research, Norwich, UK. Colonies of Lactobacillus curvatus were observed using this technique and relevant pH gradients were present when the colonies reached a diameter of about 100 microm. These pH gradients were due to production of lactic acid by L. curvatus cells in the colonies. The spatial resolution of the images was about 1.5 microm (scale of bacterial cells) and therefore very suitable for observing local effects in colonies which ranged in sizes from 1 to 500 microm.

Modelling the interactions between Lactobacillus curvatus and Enterobacter cloacae. II. Mixed cultures and shelf life predictions

The modelling approach presented in this study can be used to predict when interactions between microorganisms in homogenous systems occur. It was tested for the interaction between Lactobacillus curvatus and Enterobacter cloacae. In this binary system, L. curvatus produces lactic acid which decreases the pH in the system. The pH decrease was found to be the main limiting factor of growth of both E. cloacae and L. curvatus. This resulted in E. cloacae reaching its final concentration earlier when compared to its growth in pure culture. The models consisted of a set of first order ordinary differential equations describing the growth, consumption and production rates of both microorganisms. The parameters for these equations were obtained from pure culture studies and from literature. These equations were solved using a combination of analytical and numerical methods. The prediction of growth in mixed culture using parameters from pure culture experiments and literature were close to the experimental data. Both model predictions and experimental validation indicated that interaction occurs when the concentration of L. curvatus reaches 10(8) cfu/ml. At that moment in time, the pH had decreased to inhibiting levels. These concentrations of microorganisms (10(8) cfu/ml) do occur in fermented products where interactions obviously are important. In nonfermented foods however, this level of microorganisms indicate that spoilage has occurred or is about to start. Microbial interactions can therefore be neglected when predicting shelf life or safety of food products in most cases.