Climate change amplifies food safety risks by fostering the proliferation of pathogens and contaminants in the food supply chain, and introducing unfamiliar or novel hazards. Among the food safety threats, because of their ubiquity, MYMATCH will consider the effects of climate change on a selection of mycotoxins (related to fungi belonging to Aspergillus, Fusarium, and Alternaria) occurring in maize, wheat, tomato, and nuts. Thanks to a strong and multi-actor partnership, MYMATCH will contribute to i) the prediction and mitigation of risk related to fungi and mycotoxin occurrence, ii) the assessment of mycotoxins exposure in humans (concerning different diets) and animals, and iii) the implementation of proper risk management measures. This will be achieved with data collection taking place at different levels, from literature considering events that happened in the past, under controlled environments and open fields, enabling the generation of the missing datasets needed to fulfil the project aims. This will support the development and implementation of fungi and mycotoxin predictive models founded on accurate climate change scenarios to anticipate the changes in mycotoxin occurrence in European food systems. MYMATCH AI mycotoxin management Platform will be the final output, the support for all food system actors with tailored predictions, recommendations, and mitigation approaches. By using this platform, the agri-food researchers, farmers, industry stakeholders, and policymakers, involved in the project through the MYMACTH’s Multi-Actor Framework, will be assisted in taking threat-mitigation initiatives and in decision-making, both in the short- and strategic long-term planning. MYMATCH tools and methods will be generated in a way that is easily extendable to other contaminant issues and co-created and developed with a strong interaction with potential users like EFSA.

The main objective of MOLOKO project is the manufacturing, implementation and validation of a self-managing and automatic miniaturized integrated photonic sensor to be used as process analytical instrumentation for fast-response on-site monitoring of interest analytes for security and quality within milk supply chain. These challenging objectives are achieved by integrating within the same device platform forefront technologies as organic photonics, nanoplasmonics, immunoassay diagnostics and microfluidics. Specifically, we aim at realizing multiplexing quantitative detection of up to 10 analytes among which food safety parameters, e.g. antibiotics (i.e. penicillin, ampicillin, cephalonium) and toxins (i.e. mycotoxins and bacterial toxins) and food quality parameters e.g. lactoferrin and caseins by implementing a highly-integrated optoplasmonic-microfluidic sensor in the strategic checkpoints along the entire supply and value chain of milk. The MOLOKO miniaturized integrated photonic sensor is specifically designed according to milk primary production, processing and distribution end-users in order to enable and guarantee fast, low-cost, robust, quantitative and high-sensitive multiplexing detection for the rapid acceptance screening of milk from primary producers (farm tank milk) or single bovine. The inherent versatility of the sensor guarantees disruptive effectiveness in multiple real settings applications, i.e. as automated sensor integrated in milking machine as handheld reusable analytical instrument for specialized and non-specialised milk operators. The effectiveness and market-placement of the engineered functional prototype is quantitatively evaluated by direct comparison with respect to standard analytical methods and commercially available optical biosensors.

Analytical techniques for the measurements of chemical and microbial contaminations along the food chain require detection levels in the ppb range. A direct measurement of contaminants at theses concentrations in a food matrix is not possible by any photonics principle. PHOTONFOOD aims to overcome this barrier by developing an integrated solution that combines innovations in smart paper-based sample treatment, mid-infrared (MIR) sensing and advanced data analysis. Mid-infrared (MIR) spectroscopy has proven to be the most reliable and broadly applicable spectroscopic method for detection, characterization and quantification of chemical and microbial contamination. To transform MIR sensing from existing lab solutions into a portable solution to broad usage in the food chain, PHTONFOOD aims to develop novel infrared light sources, specifically interband cascade light emitting diode (IC-LED), and interband and quantum cascade lasers (ICL/QCL). The light sources will be combined with sophisticated waveguide technology and 3D-paper microfluidics. The project will develop a solution consisting of: (1) A mid-fidelity (MI-FI) device with an envisioned low-price range that can be used for daily monitoring, and (2) a medium-price range high-fidelity (HI-FI) device for reference analysis and accreditation. The solution will be validated and demonstrated in real scenarios for fungal and water mould (oomycetes) contamination, mycotoxins in wheat, nuts, dried fruits and aquaponic-based herbs, as well as pesticides and antibiotics in aquaponic-based herbs. The research will be performed by a multidisciplinary and highly acknowledged research team in collaboration with leading mycotoxin analysis laboratories, an aquaponics farm, a distributor of dry foods and farms with on-site food processing and vending. Strategies for transferring the obtained data and integrating it into data platforms for tracing food contamination, improving food quality and reducing waste will be developed.

DARWIN aims to contribute to a fair, healthy, safe and environmentally friendly food system by co-developing an innovative detection strategy by integrating targeted analytical PCR-based methods, untargeted sequencing methods, and digital solutions. A new generation of innovative and reliable DNA-based analytical detection methods, ranging from enhanced PCR methods for NGT detection, identification and quantification in single and multi-target systems, to untargeted sequencing methods related to whole-genome sequencing, laser capture microdissection-based sequencing and high throughput metagenomics sequencing for NGT screening and characterization. DARWIN will bring a cutting-edge approach developing genetic fingerprints using artificial intelligence to overcome challenges related to event-specific targeted methods, unambiguously identifying specific NGT lines. DARWIN also brings innovation in the new application of digital solutions such as data spaces and documentation-based screening to increase the data evidence to NGTs; blockchains to enable transparent and traceable detection along the food chain; AI models; and a Decision Support System to improve accuracy and support the selection of the best fit for purpose detection methods. DARWIN will reach TRL4-5 though validation including in-house, transferability and full trials, and presenting 3 cases representative of real situations relevant for NGT products in the EU agri-food market. After methods validation and the definition of a clear oriented detection strategy, DARWIN will theoretically extrapolate its finding to more complex scenarios encountering the diversity of NGT lines and food products that might reach EU markets. All this, under an interdisciplinary co-creation ecosystem based on Responsible Research and innovation (RRI) principals, empowering a wide variety of stakeholders - including consumers - in Europe and internationally; and robustly contributing to the policy making.