Quantifying the uncertainty of transfer of Cronobacter spp. between fomites and floors and touch points in dairy processing plants.

Cronobacter spp. are opportunistic pathogens that must be controlled in infant powder manufacturing plants. This study evaluated the spread of Cronobacter cells via contact surfaces within a dairy manufacturing environment. Transfer rates of Cronobacter spp. were determined from vectors for transmission including moveable fomites (e.g. trolley wheels and boots) and gloved hands to various types of recipient surfaces (stainless steel, linoleum and resin-coated concrete) typical for dairy manufacturing environments. Overall, with a starting inoculum of 106 CFU/mL, approximately 104 CFU/mL Cronobacter cells were transferred from each fomite onto each recipient surface during the initial transfer event. Gloved hands transferred the highest number of Cronobacter cells, followed by polyvinylchloride boots and then polyurethane trolley wheels. We demonstrate, using a combination of experimental data and uncertainty analysis, that if a movable fomite (boots or trolley wheels), or gloves became contaminated, Cronobacter could be spread over a wide area within a manufacturing plant. To the authors' knowledge, this is the first quantitative estimation of the spread of Cronobacter within a dairy manufacturing plant, that can also be practically applied as a tool for providing information in making risk management decisions. In particular, the estimation of spread suggests areas for cleaning and sanitation within a dairy manufacturing environment during a contamination event.

Status and future of Quantitative Microbiological Risk Assessment in China.

Since the implementation of the Food Safety Law of the People's Republic of China in 2009 use of Quantitative Microbiological Risk Assessment (QMRA) has increased. QMRA is used to assess the risk posed to consumers by pathogenic bacteria which cause the majority of foodborne outbreaks in China. This review analyses the progress of QMRA research in China from 2000 to 2013 and discusses 3 possible improvements for the future. These improvements include planning and scoping to initiate QMRA, effectiveness of microbial risk assessment utility for risk management decision making, and application of QMRA to establish appropriate Food Safety Objectives.

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

UNLABELLED: 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).

Quantitative risk assessment for hazards that arise from non-proteolytic Clostridium botulinum in minimally processed chilled dairy-based foods.

A modular process risk model has been constructed that describes the manufacture of dairy dessert products and hazards that arise from non-proteolytic Clostridium botulinum. The model describes batch manufacture and consumer storage of a family size generic dairy dessert but includes a realistic quantification that could apply to a specific food product. The dairy dessert sector is an expanding part of the UK market. The model includes modules that describe spore loads in raw materials, spore inactivation during thermal processing, volume partition and the population kinetics for non-proteolytic C. botulinum during sequential isothermal storage regimes. Where possible elements of uncertainty and variability are identified explicitly. The model is constructed as a belief network from published data and expert opinions. The model provides marginal probabilities, and associated sensitivities, for a range of endpoint measures centred on the toxicity of a single retail unit after an extended period of storage. The decimal reduction time for non-proteolytic C. botulinum spore populations at the highest (hold) temperature of the primary thermal process and the highest temperature experienced during poorly controlled (consumer) storage are dominant factors determining risks. Priorities for additional information to support risk assessments have been identified.

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.

Estimating risk from small inocula by using population growth parameters.

Risk from an uncertain small inoculum depends on variability of single-cell lag times. However, quantifying single-cell variability is technically challenging. It is possible to estimate this variability using population growth parameters. We demonstrate this possibility using data from literature and show a Bayesian scheme for performing this task.

Estimating single-cell lag times via a Bayesian scheme.

Network models offer computationally efficient tools for estimating the variability of single-cell lag phases. Currently, optical methods for estimating the variability of single-cell lag phases use single-cell inocula and are technically challenging. A Bayesian network model incorporating small uncertain inocula addresses these limitations.

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.

Probabilistic representation of the exposure of consumers to Clostridium botulinum 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.

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.