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

该提案的关键假设是，通过在收获时识别和量化真菌菌群和真菌毒素谱（作为真菌代谢组的一部分），并结合储存中的二氧化碳测量，并将其与与生长和真菌毒素产生的边界条件相关的生物学模型联系起来。这将使开发一种综合的收获后发展支助系统成为可能，以改进储存谷物的管理并减少废物流。目标是(a)检查包括北爱尔兰在内的英国不同地区收获的谷物，并量化真菌群落，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)检查红外CO2传感器用于监测农场粮仓和粮仓的使用情况；(c)将CO2数据与用于食品和饲料用途的谷物（小麦/大麦/玉米/燕麦）的生长/霉菌毒素生产的边界温度x水分含量模型相结合；(d)在小型和中试规模的粮仓中测试集成实时系统，不同的储存谷物具有初始安全；中间和较差的水分含量和3-D采样，以确定腐败霉菌活性的起始（真菌群落分析）和真菌毒素（游离的，共轭的（隐蔽的）真菌毒素）。为了准确的毒素定量，采样将涉及一种新型的垂直和水平组合装置，该装置将能够在传感器节点的水平上进行采样，(e)检查这种用于改进谷物收获后管理和尽量减少收获后损失的DSS工具的成本效益分析。

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.