Modeling Microbial Responses in Foods (Contemporary Food Science)

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Commercially relevant varieties will be utilized. Strains that have been associated with outbreaks from the commodities of interest will be used whenever possible. If not possible, other significant pathogenic strains will be selected. As appropriate, antibiotic-resistant variants of these strains have been isolated, and several have been modified i. Validated non-pathogenic surrogate species of various microorganisms are also available for those situations where the use of such organisms may be appropriate. Effort to screen and evaluate additional surrogate species will also be a part of this project.

Strains of different genera can be engineered to contain traits noted above as required.

The modifications will allow, when necessary, easy identification of the inoculated strains in the presence of high levels of background microflora. Additionally, any animal tissue samples or animal models used to cultivate or study some of these pathogens will also be made available.

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This is especially relevant in the case of viruses, as two recent tissue culture methods for human noroviruses have been reported Ettayebi et al. One goal of the project is to recruit a member who has access to and can utilize one of these systems. Prior to use strains are streaked onto non-selective media supplemented with selective agents as appropriate.


Food Microbiology Laboratory (Contemporary Food Science)

Inocula may be prepared from plate or broth cultures, and may or may not be washed i. Appropriate carrier media will be used for inoculations at volumes, levels and methods typical for the commodity being evaluated or assay conducted. Standard methods will also be used for viral or parasitic inocula. Methods for inoculation of food commodities will vary, as required, to best mimic standard commodity-specific criteria and the specific hypothesis-based research questions being addressed.

Recovery of Pathogens from Inoculated Samples. Sample sizes, buffering solutions, and maceration methods will vary depending upon commodity- and experiment-specific requirements. Enumeration of bacterial pathogens following serial dilution using standard plating techniques onto selective and non-selective media, Most Probable Number techniques, or by more sophisticated molecular techniques are commonly used by project PIs. The collection of quantitative data will be encouraged whenever possible and can be used to populate risk models.

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Standard methods will also be used for the recovery of viral or parasitic organisms from relevant food commodities. With respect to the viruses, the European Committee for Standardization has developed a standard method for the recovery, concentration, and detection of noroviruses and Hepatitis A virus from select food commodities vegetables, soft fruits, and oysters which will be used until the US develops its own standard or adopts the EU standard EN ISO Sampling methods to recover pathogens from the environment and foods will vary depending upon the sampling scheme and source based on experimental design.

All attempts will be made by project PIs to not only determine frequency of pathogen isolation, but also investigate concentration of identified pathogens, as concentration is a critical variable required in Objective 2. The long-term goals of this objective include i evaluating and modeling environmental parameters and indicator organisms as related to the presence of pathogenic microorganisms; ii understanding prevalence of pathogens and antimicrobial resistance within the environment, food products, food production, food processing, food distribution, and consumer systems; and iii understanding persistence, dissemination and traceability of microorganisms within the environment, food products, food production, food processing, food distribution and consumer systems.

Evaluate and model environmental parameters and indicator organisms as related to the presence of pathogenic microorganisms. Critical to the development of risk-based approaches to food safety is the understanding of how the presence and populations of pathogenic microorganisms relate to measurable physicochemical and microbial indicators. Currently employed standards throughout the food production and manufacturing sectors involve the frequent sampling for various indicator or index organisms.

However, while dogma dictates that changes in indicators or indexes result in an increased risk for a product, very little published literature on this topic is available. One of the drawbacks of testing for pathogens or microbial indicators is the interval between testing and the time of result.

In many instances, this time delay can range anywhere from 12 h to five or more days depending on target organism s that are being detected. Long detection times preclude testing from being used in real time. To address these issues, we propose to evaluate and model these relationships using available, classical methods combined with emerging detection technologies.

For instance, one could apply metagenomics for enhanced characterization of the microbial communities in food and food manufacturing environment and determine how these communities may change over time in relation to the environmental conditions associated with processing, preservation, sanitation, and storage.

Course - Reaction kinetics in food science - EFFoST

The use of strain level response data will introduce added value to quantitative microbial risk assessment QMRA and associated modeling tools. As outlined by den Besten et al. To further enhance and address some of the shortcomings of metagenomics i. Basically, metatranscriptomic sequencing detects cDNA created from RNA extracted from a given microbial community and thus identifying genes in a population that are being transcribed and maybe even translated.

Understanding prevalence of pathogens and antimicrobial resistance within the environment, food products, food production, food processing, food distribution and consumer systems. The success of any risk assessment hinges on a comprehensive understanding of both concentration and distribution of risk factors, including foodborne pathogens and presence of antimicrobial resistance genes.

Much of the currently available prevalence data is lacking critical concentration data i. Also commonly overlooked are the potential spatial-temporal population differences that may exist across the US, and offer a unique niche for PIs collaborating on this multistate project to evaluate. These spatial patterns that exist along the farm-to-fork continuum provide insight into current relative risk of food products and production environments and are a critical starting point against which all risk reduction attempts can be benchmarked. Statistically-sound sampling methods and sample sizes are of fundamental importance to all studies.

These issues will be addressed by our plan to 1 evaluate frequencies and concentrations of pathogens and antimicrobial-resistance genes and 2 identify production, manufacturing, distribution or consumer management practices that improve public health by reducing these risks.

The approaches used to address our plan will use both classical bench-science as well as surveys based in the social sciences. Understanding persistence, dissemination, and traceability of microorganisms within the environment, food products, food production, food processing, food distribution, and consumer systems. While a significant amount of data exists for some commodities, others remain relatively understudied, and handling practices are continually evolving within the industry.

For data that do exist, a systematic review to identify critical data gaps and extraction of data for meta-analyses and inclusion into comprehensive risk assessments is an opportunity for PIs of this project.

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Our strategy to tackle this concern rests in our multidisciplinary, systems approach of critical data gap identification, data generation, and modeling of multiple commodity, production, process, distribution and consumption patterns. Objective 2: Develop, validate, and apply science-based interventions to prevent and mitigate food safety threats.

This section describes the current and planned activities and methods under the risk management component of the project. Risk management is the process of applying the results of risk assessments for the control and mitigation of foodborne pathogens, including regulatory action.

This project is designed as a systems approach to food safety. More specifically, this is the process of studying discrete but interrelated sections of the farm-to-table continuum to provide comprehensive and integrative solutions to complex food protection issues. Along with source and food attribution data, the recently published scheme for categorizing foods implicated in foodborne disease outbreaks Richardson et al.

This will in turn reduce the risk of foodborne illness to the consumer. To accomplish the tasks associated with Objective 2, a risk management framework based on commodity-specific flow diagrams and inputs from Objective 1 will be developed.

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A key component of this activity will be the use of risk modeling techniques to relate the levels of microbial contamination in food to the likelihood of a foodborne illness or outbreak occurring. The information developed using this approach will then be used to development of specific interventions for risk mitigation at specific points along the farm-to-fork continuum. The results of the risk modeling approaches will also help identify critical data gaps, which will feed back into new projects under Objective 1. Models and risk management.

Predictive microbiology and QMRA are rapidly developing scientific disciplines that use mathematical equations, numerical data, outbreak data, and expert elicitation to estimate the presence, survival, growth, and death of microbes in foods. These models allow for the prediction of the safety of a product based on the entire sequence of events up to consumption, including worst-case food handling scenarios. They provide a framework for evaluating the effectiveness of risk-reduction strategies. Multiple extrinsic factors affect microbial growth and survival in foods.

For example, many antimicrobial interventions rely on thermal destruction of microbial cells for product safety. Because temperature may change drastically during processing, storage, and distribution, pathogen predictive models will be developed to describe microbial behavior in various food commodities, especially those subjected to novel thermal interventions e. These models will be validated using real-life scenarios whenever possible.

Microbial predictive models will be developed using kinetics derived from microbial growth experiments under different conditions, including not only temperature but the interaction with other intrinsic and extrinsic factors. The models generated for one commodity can be used to guide a series of experiments to validate the model for different, closely related commodities. Following the development of temperature and other models, expert opinion, industry, experimentally-derived and published, peer-reviewed data for processing and handling conditions to the point of consumption can be integrated into risk assessment models to estimate changes in microbial population dynamics.

This will also be a critical point to integrate microbial community data as well as strain-level data using metagenomics and metatranscriptomics as discussed in Objective 1. Briefly, published data are collected during a thorough search of medical and biological databases for documents related to the food commodity. Data from this objective and Objective 1 as well as the peer-reviewed literature will be translated into appropriate discrete or probability distribution functions and assigned to processes in the commodity flow diagram.

Statement of Issues and Justification

The QMRA model can be created using a variety of constantly evolving software tools. Tornado analysis can then be used to determine the relative significance of the input variables. Another useful technique is the use of Bayesian networks and inference to build gene regulatory networks that could help, for example, 1 predict the duration of lag growth phase of a given foodborne pathogen or 2 overcome lack of biological data which drives the uncertainty in the most QMRA models.

Risk Mitigation.

Modeling Microbial Responses in Foods (Contemporary Food Science) Modeling Microbial Responses in Foods (Contemporary Food Science)
Modeling Microbial Responses in Foods (Contemporary Food Science) Modeling Microbial Responses in Foods (Contemporary Food Science)
Modeling Microbial Responses in Foods (Contemporary Food Science) Modeling Microbial Responses in Foods (Contemporary Food Science)
Modeling Microbial Responses in Foods (Contemporary Food Science) Modeling Microbial Responses in Foods (Contemporary Food Science)
Modeling Microbial Responses in Foods (Contemporary Food Science) Modeling Microbial Responses in Foods (Contemporary Food Science)

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