Topics

Integrating Omics in QMRA: predicting growth and microbial physiology through Omic data OMICS; Quantitative microbiological risk assessment (QMRA) methodology aims to estimate and describe the transmission of pathogenic microorganisms from animals and food to humans. In microbiological literature, the availability of whole genome sequencing (WGS) data is rapidly increasing, and incorporating this data into QMRA has the potential to enhance the reliability of risk estimates. Making sense of data:  the use of Data Science, AI, Machine Learning, and their tools for decision making in the Food sector  Data Science: "Data science is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from potentially noisy, structured, or unstructured data". Reusing existing data, models, and technologies is crucial for researchers in food microbiology, risk assessment agencies, and food company operators. It is still difficult to reuse this kind of information since most food safety data sets, models, and tools are only accessible in formats that are exclusive to certain platforms or programmes, and these formats seldom meet the Findability Digital twins in the Food Sector   Digital twins play a pivotal role in enhancing food safety and quality by providing a dynamic and virtual representation of the entire food production process. In the realm of food industry, digital twins serve as digital replicas of physical systems, enabling real-time monitoring, analysis, and optimization of various stages in the food supply chain. These sophisticated models allow for the integration of data from sensors, IoT devices, and other sources to create a comprehensive view of every aspect of food production, from farm to fork. This technology empowers stakeholders to identify potential risks, track product quality, and respond swiftly to deviations from desired standards. By simulating and analysing different scenarios, digital twins facilitate proactive decision-making, helping in the prevention of contamination, spoilage, or other issues that may compromise food safety. Additionally, digital twins contribute to supply chain transparency, traceability, and overall efficiency, ensuring that consumers can trust the safety and quality of the food they consume.