Funded under the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.3, Theme 10.
Marine toxins in algal food supplements and ingredients
Coordinator
The task includes evaluation of safety parameters in traditional and novel foods through the development of: a) chemical sensors and immunosensors for the selective detection of algal and plant toxins, and trace allergens; b) portable devices based on laser photoacoustic spectroscopy (LPAS) and other spectroscopy techniques; c) Ambient Desorption Ionisation methods with High-Resolution Mass Spectrometry (DESI-HRMS); e) use of rt-PCR and digital droplet-PCR to evaluate new and (re)-emerging foodborne pathogenic species; f) metabolomics and proteomics strategies coupled to pathway analysis to evaluate the effects of emerging and re-emerging contaminants; d) analytical techniques, i.e., spectroscopic and MS-based, to determine biogenic amines, pesticides, veterinary drug residues, mycotoxins and processing toxicants; and g) new Matrix-Reference Materials to be characterised for food safety parameters will be developed, including preparation of test-lots, their characterization and homogeneity and stability studies.
Safety assessment of traditional and novel foods through targeted and untargeted methodologies (M36)
Dietary supplements and innovative ingredients based on blue-green algae are marketed as natural healthy products. They generally contain non-toxic cyanobacteria, but cultivation without appropriate quality controls allow contamination by toxin producer species present in the natural environment.
Although the adverse effects related to the exposure to marine toxins are known, occurrence data and monitoring plans are scant, due to the lack of proper analytical methods, standards and reference materials.
In this context, the development and interlab/interplatform validation of large Cross Collision Section (CCS) database obtained by high resolution -ion mobility mass spectrometry can provide analytical support. Being CCS values independent from the matrix, their use as an additional point of recognition could be useful for screening purposes. In this context, the calculation of theoretical CCS values based on artificial intelligence may support the robust identification of compounds.
The proposal will involve the following steps: