Food and Feed Quality Unit (U15), Valorisation of Agricultural Products Department (D4), Walloon Agricultural Research Centre (CRA-W), Henseval Building, 24 Chaussée de Namur, 5030, Gembloux, Belgium,
Anal Bioanal Chem. 2013 Sep;405(24):7765-72. doi: 10.1007/s00216-013-6775-7. Epub 2013 Feb 13.
In recent years, near-infrared (NIR) hyperspectral imaging has proved its suitability for quality and safety control in the cereal sector by allowing spectroscopic images to be collected at single-kernel level, which is of great interest to cereal control laboratories. Contaminants in cereals include, inter alia, impurities such as straw, grains from other crops, and insects, as well as undesirable substances such as ergot (sclerotium of Claviceps purpurea). For the cereal sector, the presence of ergot creates a high toxicity risk for animals and humans because of its alkaloid content. A study was undertaken, in which a complete procedure for detecting ergot bodies in cereals was developed, based on their NIR spectral characteristics. These were used to build relevant decision rules based on chemometric tools and on the morphological information obtained from the NIR images. The study sought to transfer this procedure from a pilot online NIR hyperspectral imaging system at laboratory level to a NIR hyperspectral imaging system at industrial level and to validate the latter. All the analyses performed showed that the results obtained using both NIR hyperspectral imaging cameras were quite stable and repeatable. In addition, a correlation higher than 0.94 was obtained between the predicted values obtained by NIR hyperspectral imaging and those supplied by the stereo-microscopic method which is the reference method. The validation of the transferred protocol on blind samples showed that the method could identify and quantify ergot contamination, demonstrating the transferability of the method. These results were obtained on samples with an ergot concentration of 0.02% which is less than the EC limit for cereals (intervention grains) destined for humans fixed at 0.05%.
近年来,近红外(NIR)高光谱成像技术已被证明适用于谷物行业的质量和安全控制,因为它可以在单颗粒水平上采集光谱图像,这对谷物检测实验室非常有吸引力。谷物中的污染物包括杂质(如秸秆、其他作物的谷物和昆虫)以及不希望存在的物质(如麦角)。对于谷物行业而言,麦角因其含有生物碱而对动物和人类具有很高的毒性风险。曾开展过一项研究,基于麦角的近红外光谱特征,开发出一种完整的检测谷物中麦角体的方法。这些特征被用于建立相关决策规则,这些规则基于化学计量学工具和从近红外图像中获得的形态学信息。本研究旨在将该程序从实验室级别的在线近红外高光谱成像系统转移到工业级别的近红外高光谱成像系统,并对后者进行验证。所有进行的分析表明,使用两种近红外高光谱成像相机获得的结果都非常稳定且可重复。此外,通过近红外高光谱成像获得的预测值与立体显微镜法(参考方法)提供的预测值之间的相关性大于 0.94。对盲样进行的转移方案验证表明,该方法能够识别和量化麦角污染,证明了该方法的可转移性。这些结果是在麦角浓度为 0.02%的样品上获得的,该浓度低于人类食用的谷物(干预谷物)中麦角的 EC 限量(0.05%)。