This webinar was co-organized by Cornell University and the Walmart Foundation as part of the Cornell University-led project "Food Security Strategies for an Emerging E-commerce Economy: Modelling Approaches to Predict Shelf-life, Food Quality, and Food Safety in China." During the webinar, representatives from Cornell University and Shanghai University of Technology and representatives from Chinese agricultural producers and processors discussed microbial food spoilage and food safety, microbial modeling, and related case studies.
1st Session: Introduction to Microbial Food Spoilage and Safety
Spokesperson: Martin Wiedmann, Dr. med vet., PhD., Gellert Family Professor of Food Safety, Cornell University
Summary:
- Food systems approach to food safety and to reducing food loss and waste is essential, but often missing
- Many tools needed to improve food safety and reduce food waste are available
- Implementation and infrastructure are needed
- It is time to think about a “digital” food infrastructure
- Trade-offs need to be considered
- Not every strategy that reduces food waste and foodborne illness will be a net positive for the world
- Consideration and quantification of residual risk is essential
- Even the best system will not be risk free
2nd Session: Introduction to Microbial Modeling
Spokesperson: Renata Ivanek, DVM, MS PhD, Associate Professor of Epidemiology, Population Medicine and Diagnostic sciences, Cornell University
Summary:
- A model is a simplified representation of the real-world (e.g., contamination of a food package).
- Mathematical models describe the real-world (e.g. microorganisms in foods) with equations/rules
- Agent-based models and risk assessments are two types of simulation models.
- Predictive microbiology is often used to support simulation models
- Simulation models use probability distributions as values of model inputs
- Predictions of simulation models are probability distributions
- Predictions represent the inherent uncertainty and variability of the model
- Models must be validated before using them for prediction
- Predictions of simulation models are probability distributions
- Model predictions should support decision making, not be taken literarily
3rd Session: Examples of Food Spoilage and Food Safety Models
Spokesperson: Qingli Dong, University of Shanghai for Science and Technology
Topic: Progress of Listeria monocytogenes risk modeling
Summary:
- Prevalence of Listeria monocytogenes
- Listeria monocytogenes is highest in meat product in China
- Meta-analysis is needed for further studies
- Predictive models of Listeria monocytogenes
- Competitive models with other bacteria are useful
- Single-cell models should be well-related to population cell
- Cross-contamination might be considered for modelling
- MicroRisk Lab interactive freeware is easy to use
- Risk assessment and management of Listeria monocytogenes
- Bayesian approach demonstrates its ability to provide significant benefits for QMRA
Spokesperson: Martin Wiedmann, Dr. med vet., PhD., Gellert Family Professor of Food Safety, Cornell University
Topic: Examples of Food Spoilage and Food Safety Models
Summary:
Dr. Wiedmann brought up a few more exciting and comprehensive examples on Listeria risk assessments, which hopes to give the audience an idea of the world that lies ahead of us with regard to applying different types of models to rebuild and develop more sustainable and safer food systems.
A wide range of models are available to help industry make food safety and spoilage decision.
Integration of tools into more complete digital twins is the next frontier.
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This seminar is one of the academic activities of Cornell University's Food Security Strategies for an Emerging E-commerce Economy: Modelling Approaches to Predict Shelf-life, Food Quality, and Food Safety in China. Funded by Wal-Mart Foundation, the project looks to build technical and economic models which tackle microbiological food safety and quality issues in China. Based on primary data, the models offer a novel approach to predict the shelf-life, quality, and safety of Chinese fresh produce.