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

这一建议的关键假设是，通过识别和量化收获时的真菌生物群和真菌毒素谱(作为真菌代谢组的一部分)，使用储存的二氧化碳测量的组合，并将其与与生长和真菌毒素产生的边界条件有关的生物模型联系起来。这将使开发一个综合的收获后决策支持系统成为可能，以改进对储存谷物的管理并减少废物流。目标是(A)检查英国不同地区(包括北爱尔兰)收获的谷物，并量化真菌生物群，以确定这些物种产生的主要产毒/腐败霉菌和毒素与收获时的天气条件有关；(B)检查使用红外二氧化碳传感器监测农场粮食储存和储存的情况；(C)将二氧化碳数据与用于食品和饲料的谷物(小麦/大麦/玉米/燕麦)的生长/霉菌毒素生产的边界温度x水分模型合并；(D)在小规模和试点规模的粮食筒仓中测试综合实时系统，以确定腐败霉菌活动(真菌生物群分析)和真菌毒素(游离、结合(蒙蔽)真菌毒素)的产生原因。为了准确地量化毒素，采样将涉及一种新的垂直和水平相结合的装置，该装置将能够在传感器节点一级进行采样；(E)审查这种决策支持系统工具的成本效益分析，以改进谷物的收获后管理，并将收获后损失降至最低。

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 andin 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.