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

为储存的谷物开发一种新颖有效的收获后决策支持系统（DSS），以最大限度地减少食品中的霉菌腐败和霉菌毒素

• 批准号: 2450877
• 金额: --
• 依托单位: CRANFIELD UNIVERSITY
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2450877
• 关键词: Development; novel; effective; post; harvest

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.

该提案的关键假设是，通过在收获时识别和量化真菌生物群系和真菌毒素谱（作为真菌代谢物组的一部分），并在储存中使用CO2测量的组合，并将其与生长和真菌毒素产生的边界条件相关的生物模型联系起来。这将有助于制定一个综合的收获后决策支持系统，以改善对储存谷物的管理，并减少废物流。目的是（a）检查英国不同地区（包括北爱尔兰）收获的谷物，并对真菌群落进行量化，以确定这些物种在收获时的天气条件下产生的主要致腐/腐败霉菌和毒素;（B）检查红外二氧化碳传感器用于监测农场谷物储存和筒仓的使用，（c）将CO2数据与用于食品和饲料用途的谷物的生长/霉菌毒素产生的边界温度x水分含量模型相结合（小麦/大麦/玉米/燕麦），（d）在具有初始安全的不同储存谷物的小型和中试规模谷物筒仓中测试集成实时系统，中等和较差的水分含量和3D采样，以确定腐败霉菌活动（真菌生物群系分析）和真菌毒素（游离、结合（掩蔽）真菌毒素）的起始。为了准确的毒素定量，采样将涉及一种新的垂直和水平相结合的装置，这将使传感器节点的水平采样，（e）审查成本效益分析，这种决策支持系统的工具，以改善谷物收获后管理和尽量减少收获后的损失。