The growth of microorganisms in food products is affected by environmental conditions such as temperature of storage, water activity, acidity and humidity. The development of predictive models for your products and experimental data can help you control the safety of the product and minimise the risk of microbial contamination.
Making decisions about the safety of the product can be difficult, especially if environmental conditions vary during the production and/or storage period.
Quantitative Microbial Risk Assessment (QMRA) requires knowledge of both the hazard and the exposure. Predicted microbial concentrations in foods can be linked to food consumption and dose response models using our software Creme Food Safety. This produces a more complete profile of risk and allows for better decision making and focusing of resources in risk mitigation.
Problem areas / challenges
Quantitative Microbial Risk Assessment (QMRA)
- Simulate actual presence in a probabilistic manner to reflect actual consumer habits & practices. Using a worst case scenario methodology creates unrealistic results.
- Understanding the impact of ingredient/additive usage in a food or packaging material. Our models can simulate a bespoke scenario with and without the usage and compare the two.
- Accessing useful data. Our data includes nationally representative consumption data from the US, EU, China, Brazil, Mexico, and other countries.
- A competent authority may be unsure of who are most at risk of exceeding exposure limits are. Bespoke analysis can determine intakes per age, sex, BMI, health status including pregnancy, smoking etc and easily identify who are the high consumers with the highest exposures.
Predictive microbiology
- Control bacteria in the food supply from farm to fork including maximising shelf life. Our models and data can inform every step of the process.
- Ensure both food processing and storage conditions control pathogens. We can predict pathogen growth under different conditions like temperature.
- Use our in-house expert team of food and data scientists to address specific questions relevant to you. Avail of custom and bespoke statistical analyses and exploratory model development.
- Take full advantage of your experimental data to predict impact on food safety and develop bespoke models that will help you make decisions quicker.
- Experimental data can be modelled and mined to take full advantage of what can be learned from the data. When experimental and study data is provided which clearly defines the stability of a product (i.e. the product is considered stable for a particular combination of additives etc., but unstable for some other), we can develop a predictive model which interpolates between different input variables and provides the probability that the analysed product is stable.
- We develop user friendly software where users can test how changing factors affect the growth of microorganisms in a product. Models are unique to your experimental, laboratory and study data.Creme Microbial models include:
- Determination of factors (temperature, pH, water activity) required to inhibit development of pathogens.
- Fitting growth models to experimental data- modelling of the lag, exponential, stationary and death phase (Primary Models).
- Growth simulation for given food and factors (temp, pH, aw). Factors can be either constant or vary over time (Secondary Models).
- Modelling of bacterial decay and thermal inactivation models.
Genomic Sequencing
- Use predictive models and machine learning to enhance food quality and safety approaches using whole genome sequencing data.
- Mitigate against the risk of bacterial contamination in the food supply chain in a smarter, faster and more specific way.
Our Data Capabilities
- Centralise and house all food safety data in the cloud and on one application.
- Use Creme Global as a trusted third party for gathering industry-wide data at a trade association level.
- Collate, capture and validate data across the supply chain at all points in the farm-to-fork continuum with our expert services and domain knowledge.
- Data collection and data portal development for sensitive and commercial data.
- Trend and analyse data over time and space, develop predictive models and understand where vulnerabilities lie.
Questions
- In what conditions should the product be produced and stored in order to minimise the growth of considered microorganisms?
- How do the microorganisms respond to the environmental conditions?
- What is the optimal length of the shelf-life?
- Which product formulations are most likely to give rise to bacterial growth and under which conditions?
- What controlling factors should be applied in order to minimise the risk of pathogen growth? Models can be used to either select one controlling factor (i.e. use water activity to control the growth of pathogens) or use multiple factors simultaneously.
- What temperature should the product be stored in?
