中文摘要：镰刀菌是谷物中常见的真菌病害，可导致大麦和小麦产量降低。本研究利用近红外反射光谱技术和统计预测模型对镰刀菌污染的谷物样品进行了快速无损检测。实验发现，该方法的分类正确率（CCR）达到了98%甚至更高，当应用二阶和三阶导数预处理时，鉴别预测的准确性得到改善。结果表明，利用光谱技术和PLS-DA鉴别模型可以快速鉴别镰刀菌污染的谷物。
外文摘要：Fusarium is a common fungal disease in grains that reduces the yield of barley and wheat. In this study, a near infrared reflectance spectroscopic technique was used with a statistical prediction model to rapidly and non-destructively discriminate grain samples contaminated with Fusarium. Reflectance spectra were acquired from hulled barley, naked barley, and wheat samples contaminated with Fusarium using near infrared reflectance (NIR) spectroscopy with a wavelength range of 1175-2170 nm. After measurement, the samples were cultured in a medium to discriminate contaminated samples. A partial least square discrimination analysis (PLS-DA) prediction model was developed using the acquired reflectance spectra and the culture results. The correct classification rate (CCR) of Fusarium for the hulled barley, naked barley, and wheat samples developed using raw spectra was 98% or higher. The accuracy of discrimination prediction improved when second and third-order derivative pretreatments were applied. The grains contaminated with Fusarium could be rapidly discriminated using spectroscopy technology and a PLS-DA discrimination model, and the potential of the non-destructive discrimination method could be verified.
外文关键词：Fusarium；near infrared；discrimination；hulled barely；naked barley； wheat
作者：Lim, Jongguk；Kim, Giyoung；Mo, Changyeun等
作者单位：Chonbuk National University
期刊名称：SENSORS
期刊影响因子：2.677
出版年份：2018
出版刊次：1
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