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

Abstract

Cereal grains are widely consumed for their nutritional value as food and feed, and are essential in the food supply chain. However, changing climatic conditions have made these crops increasingly susceptible to fungal attacks, elevating the risk of contamination by mycotoxins—often referred to as "invisible mould poison." This can threaten grain safety and quality, posing health risks to humans and animals, and contributing to food insecurity and economic instability. This thesis examines the effects of different abiotic factors (water activity- aw and temperature) on the ratios of regulated and conjugated mycotoxin concentrations in naturally contaminated and irradiated wheat grains inoculated with Fusarium graminearum. Contaminated samples were analysed with Liquid Chromatography Tandem Mass Spectrometry. Deoxynivalenol-3-glucoside concentrations were significantly different from its precursor deoxynivalenol at 0.93 aw (22% moisture content- MC) at 25 °C in the naturally contaminated wheat with a ratio proportion of 56:44, respectively. This research further investigates the effects of different aw and temperature on CO2 production, fungal growth, and mycotoxin contamination in mini-silos of grains. It hypothesizes an integrated sensing approach (combining CO₂, temperature, and relative humidity measurements) as a decision support system (DSS) tool in real-time monitoring of CO₂ produced in stored grains would predict risks of mycotoxin contamination exceeding legislative limits. Findings show that in naturally contaminated and inoculated (Penicillium verrucosum and Fusarium langsethiae) wheat and oat grains, respectively, an increase in aw significantly increased the respiration rates (RR) and mycotoxin (ochratoxins, type A trichothecenes and their conjugate concentrations. Their legislative limits were exceeded at ≥ 0.80 aw (16% MC) with RR ≥ 15 µg CO₂ kg¯¹ h¯¹ . This research provides novel preliminary data for stored wheat and oats that can combine with other pre-harvest modules to develop a cost-effective DSS tool to improve grain storage management.