Detecting fungal contamination of cereal grains using Biospeckle Laser Technology

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The Canadian agricultural industry is valued at over $20 billion, with cereal crops representing the most widely exported commodity. Canada produced 37.5 million tonnes of wheat and 10.2 million tonnes of barley in 2013, ranking 9th in world cereal production. The occurrence of fungal pathogens in cereals drastically reduces their value and utility. In 2012, approximately 7%, 9%, and 21% of crops harvested in Alberta, Saskatchewan, and Manitoba, respectively, were downgraded due to fungal presence in or on seeds. Prediction and assessment of the potential damage due to these fungi after harvest and during shipment would be of immense value to the grain industry. In this research, we propose to develop a non-destructive optical method in an attempt to analyze fungal infection in grains. The method, called Biospeckle Laser Technology (BLT), detects changes within seeds by illuminating the specimen with coherent light and capturing the resultant interference pattern, known as biospeckle, at various time intervals and comparing that to a healthy control. The latent (visibly undetectable) presence of the fungi in the seed should lead to a deviation in the biospeckle pattern, caused by growth of the fungus and/or changes in seed composition and cellular structure resulting from infection. The severity of fungal infection in the samples can be correlated with temporal biospeckle pattern changes. The results from the proposed work should establish which biological processes of the invading fungi cause the biospeckle deviation, and how this can be quantified to result in a diagnostic evaluation. The technology should be applicable to the grain industry by providing sensitive fungal detection capability and establish an alternative method for fungal analysis other than currently used methods. The use of BLT as a sensitive method for fungal detection should enhance quality assurance for grain producers, permitting rapid quality assessment of grain shipments. The results should also be applicable for detection of latent fungal infections in other agricultural products using laser technology.