“In-depth knowledge and understanding of silo microclimate is crucial. An efficient method for tackling this is through the combination of field measurements and computer simulation based on Computational Fluid Dynamics (CFD) models. CFD is a branch of fluid mechanics that uses numerical analysis and data structures to solve and analyze problems that involve fluid flows and heat transfer. Fast computers are used to perform the calculations required to simulate the interaction of liquids and gases with surfaces defined by boundary conditions. To evaluate accurately the storage structure interaction with its surroundings, the computational model should integrate weather forecast for the specific location and time period.”

Dr. Efstathios Kaloudis Phycist with Ph.D. on Computational Fluid Dynamics Centaur Analytics, Inc. s.kaloudis@centaur.ag

MYCOTOXIN CONTAMINATION IN THE FOOD SUPPLY CHAIN Mycotoxins are poisonous compounds produced by certain species of fungi growing on grain and feed products when stored in unsafe moisture content conditions. Mycotoxins cause, at very low dosages (parts per million (ppm) or parts per billion (ppb)), a variety of human and animal health problems. The ingestion of mycotoxins can produce both short-term and chronic toxicities ranging from death to chronic interferences of the central nervous, cardiovascular, pulmonary systems, and of the alimentary tract [1].

The mycotoxins have attracted worldwide attention, over the past 30 years, firstly because of their perceived impact on human health, secondly because of the economic losses accruing from condemned foods/feeds and decreased animal productivity and, thirdly, because of the serious impact of mycotoxin contamination on internationally traded commodities. It is estimated, for example, that the cost of managing the mycotoxin problem on the North American continent is approximately $5 billion [2].

Table 1: Molds and mycotoxins of world-wide importance [4]

MOLD DEVELOPMENT AND MYCOTOXIN PRODUCTION ON POST-HARVEST GRAIN STORAGE The major factors that determine whether grain in storage would be invaded sufficiently by fungi are: • grain moisture content • grain temperature • storage time period • amount of broken grains and foreign materials present • the degree to which the grain already has been invaded by fungi before it arrives at a given site • presence of insects and mites

Figure 2: Time variation of weather conditions at the silo site: solar radiation, temperature, relative humidity, atmospheric pressure and wind velocity

All these factors interact with one another to some extent, but the major determinants are moisture content, relative humidity, temperature and time [3]. Among the fungal species that may contaminate grain, some of them will be able to produce mycotoxins. Some of the molds and their associated mycotoxins that are currently considered to be of worldwide importance are shown in Table 1.

Figure 1: Example of wireless sensor installation inside a silo feeding the predictive algorithm with real-time data

THE CHALLENGE The scientific literature [5] suggests that a management strategy of prevention of mold spoilage and mycotoxin contamination of stored grain should consists of the following steps: • Identification of critical storage situations enabling mold growth • Monitoring early signs of fungal activity • Preventive measures and anticipation of mycotoxigenic mold growth • Practical solutions for the reduction of existing contamination (by mold and/or mycotoxins)

PREDICTING MOLD DEVELOPMENT In view of the above, in-depth knowledge and understanding of silo microclimate is crucial. An efficient method for tackling this is through the combination of field measurements and computer simulation based on Computational Fluid Dynamics (CFD) models. CFD is a branch of fluid mechanics that uses numerical analysis and data structures to solve and analyze problems that involve fluid flows and heat transfer. Fast computers (typically on the cloud) are used to perform the calculations required to simulate the interaction of liquids and gases with surfaces defined by boundary conditions. To evaluate accurately the storage structure interaction with its surroundings, the computational model should integrate weather forecast for the specific location and time period.

Additionally, the algorithm process temperature and relative humidity data coming from sensors, like the ones developed by Centaur Analytics, Inc. (Figure 1). The devices are equipped with wireless connectivity with the ability to transmit data frequently (e.g. every 2 hours) from inside stored grain. The data were transmitted in real time to a cloud platform enabling instant and automated grain condition predictions.

APPLICATION EXAMPLE To illustrate the capabilities of the approach and the way it addresses all the challenges described above, a typical grain storage scenario is presented. The silo under consideration was located at Topeka, Kansas (USA) and the storage period started in the summer (Figure 2). The steel silo diameter was D=8 m / 26ft and its height was H=16 m / 52 ft. The initial temperature and moisture content of wheat were 25 oC / 77 oF and 13.6 % respectively.

The model predicts the temperature and moisture content profiles for the entire storage structure. As already mentioned above this information is correlated with mold development models that calculate the areas inside the silo with a higher risk of mold development, potentially leading to toxin contamination of the grain. Figure 3 presents these profiles after 3 and 6 months of storage time A video of the forecast for the entire storage period can be viewed in the following link: https://youtu.be/NMJMip0-VW4 . It is evident that temperature and moisture content profiles are non-uniform inside the structure. Higher temperature values are present at the core of the silo since the grain outer layers are cooled by the ambient temperature. Moisture accumulation is predicted near the grain surface after 3 months leading to an area with a high risk of mold development at the end of 6 months.

Figure 3: Grain temperature, moisture content and mold risk profiles after 3 months (top) and 6 months (bottom) of storage. A video of the simulation is also available in the following link: https://youtu.be/NMJMip0-VW4

THE BENEFITS Wireless sensor technology combined with advanced data analysis techniques provide predictions of the grain temperature, moisture content, and risk of mold development throughout the storage period, bringing unprecedented benefits to the users:

• prevention of mold development and mycotoxin contamination • cost-effective prevention strategies for mold development • higher commodity market value • supply chain transparency

REFERENCES [1] World Health Organization (WHO), https://www.who.int/news-room/fact-sheets/detail/mycotoxins [2] Food and Agriculture Organization (FAO), Grain storage Techniques – Evolution and trends in developing countries. Agricultural Services Bulleting No. 109 [3] FAO, Mycotoxin prevention and control in foodgrains [4] Hurburgh Jr, C. R. Mycotoxins in the Grain Market, Iowa State University – Extension and Outreach. [5] Lessard, F. (2015). Integrated approach of the prevention of mould spoilage risks and mycotoxin contamination of stored grain – a European perspective. 10th IOBC-WPRS International Conference 2015 on Integrated Protection of Stored Products, At Zagreb, Croatia.