Detecting fungal contamination of cereal grains using Biospeckle Laser Technology

加拿大的农业价值超过200亿美元，其中谷类作物是最广泛的 出口商品。2013年，加拿大生产了3750万吨小麦和1020万吨大麦， 世界谷物产量居第9位。谷物中真菌病原体的出现极大地减少了它们的 价值和效用。2012年，艾伯塔省、萨斯喀彻温省和 马尼托巴省分别由于种子中或种子上存在真菌而被降级。预测和评估 这些真菌在收获后和运输过程中可能造成的损害对 粮食产业。在这项研究中，我们建议开发一种非破坏性的光学方法，试图 分析谷物中的真菌感染情况。这种方法被称为生物斑点激光技术(BLT)，可以检测到变化 通过用相干光照射样品并捕获所产生的干涉图案在种子内， 被称为生物斑，在不同的时间间隔，并将其与健康对照组进行比较。潜伏的(明显的) 种子中真菌的存在应该导致生物斑点图案的偏差，原因是 真菌的生长和/或感染引起的种子成分和细胞结构的变化。这个 样本中真菌感染的严重程度可以与时间生物斑点模式的变化相关联。这个 拟议工作的结果应该确定入侵真菌的哪些生物过程导致 生物斑点偏差，以及如何量化这一点以导致诊断评估。这项技术应该 通过提供灵敏的真菌检测能力，适用于粮食行业，并建立 真菌分析的替代方法，而不是目前使用的方法。BLT作为一种灵敏方法的使用 对于真菌检测，应加强对粮食生产者的质量保证，允许快速质量评估 谷物出货量。这一结果也应适用于其他地区潜在真菌感染的检测 农产品采用激光技术。

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.