Funded under the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.3, Theme 10.
Funded under the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.3, Theme 10.
Development of novel IoT systems based on Mox Gas Sensors to support c...
Enhancement of food quality and shelf life within the catering system (public canteens and fast-food chains) as well as the distribution system (e.g., retails) to improve products and consumption models offered also taking advantage from smart sensor-based procedures and new strategies targeting "inclusion and awareness” of the consumer (in connection with Spoke 2 and 7) and nutritional information. Activities start from analysis of overall food quality of mass food catering (e.g., public canteens), main fast-food chains and the large-scale distribution to redirect towards sustainable diet/menus/products and personalised/ precision nutrition (in connection with Spoke 1, 2 and 5).
Development of novel IoT systems to support catering (M24)
Ready to eat food products and food served in canteens or catering are difficult to be protected in terms of chemical and biological risks, due to the complexity of the food itself, the conditions of storage and the intervention of multiple operators possibly leading to easy cross contaminations. The objective of this activity is to develop optimized instrument devices to control the chemical and biological risks throughout the monitoring of volatile compounds to support catering services.
A new generation of gas sensors using different materials and structures will be tested in order to identify the most performing array to monitor and detect volatile markers related to specific biological/chemical risks. Volatilomics of the different samples will be explore in order to select the most accurate sensor array, also creating a IoT real time monitoring device. Lab test will be then run with controlled gases in order to test the sensitivity of the prototypes, that furtherly will be implemented. Data analysis will be performed in order to train artificial neural networks and to develop proper predictive algorithms. Furtherly, final assembled and AI equipped IoT devices will be able to reveal the targeted markers in real time to send an alarm before the appearance of the chemical or biological risks that could happened during the secondary shelf life.
The main expected result of this work it is to create sensor arrays in order to prototype a new technology based in the application of novel sensors and AI algorithms to predict through the identification of volatile markers the relative biological/chemical risks outbreaks during the secondary shelf life to support catering.
