Development of a novel and effective post-harvest decision support system (DSS) for stored cereals to minimise mould spoilage and mycotoxins in food

The key hypothesis of this proposal is that by identifying and quantifying both the mycobiome and the mycotoxin profile (as part of the fungal metabolome) at harvest and using a combination of CO2 measurements in store and linking this to biological models related to boundary conditions for growth and mycotoxin production. This will make possible to develop an integrated post-harvest DSS for improved management of stored cereals and reduce waste streams. The objectives are to (a) examine harvested cereals in different parts of the UK including N.Ireland and quantify mycobiomes to identify dominant toxigenic/spoilage moulds and toxins produced by these species in relation to weather conditions at harvest; (b) examine the use of infra-red CO2 sensors for monitoring on-farm grain stores and
in silos, (c) integrate CO2 data with boundary temperature x moisture content models for growth/mycotoxin production in cereals destined for food and feed use (wheat/barley/maize/oats), (d) testing of the integrated real-time system in small and pilot scale grain silos with different stored cereals with initial safe, intermediate and poor moisture contents and 3-D sampling to identify initiation of spoilage mould activity (mycobiome analyses) and mycotoxins (free, conjugated (masked) mycotoxins). For accurate toxin quantification, sampling will involve a novel combined vertical and horizontal device which will enable sampling at the level of the sensor nodes, (e) examine the cost-benefit analyses of such a DSS tool for improved post-harvest management of cereals and minimisation of post-harvest losses.