Near-Infrared Spectroscopic Method for Identification of Fusarium Head Blight Damage and Prediction of Deoxynivalenol in Single Wheat Kernels
Near-Infrared Spectroscopic Method for Identification of Fusarium Head Blight Damage and Prediction of Deoxynivalenol in Single Wheat Kernels
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DOI：
10.1094/cchem-01-10-0006
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发表时间：
2010-11-01
期刊：
影响因子：
2.4
通讯作者：
Dowell, F. E.
中科院分区：
文献类型：
--
作者：
Peiris, K. H. S.;Pumphrey, M. O.;Dowell, F. E.
镰刀菌头枯病（FHB）或赤霉病可导致小麦（Triticum aestivum L.）显著的作物产量损失和谷物污染。种植感病性较低的品种是控制FHB和降低谷物中脱氧雪腐镰刀菌烯醇（DON）水平的最有效方法之一，但育种计划缺乏快速和客观的方法来鉴定真菌和毒素。估计谷物中健全籽粒和镰刀菌受损籽粒（FDK）的比例以及估计FDK的DON水平对于客观评估品种的抗性非常重要。一个自动化的单核近红外（SKNIR）光谱法鉴定FDK和估计DON水平进行了评估。SKNIR系统分类视觉声音和FDK的准确率分别为98.8%和99.9%。声音部分没有或很少积累DON。将FDK级分分选成具有高或低DON含量的级分。SKNIR系统识别的FDK籽粒与其他FHB评价指标如FHB严重度、FHB发病率和每克籽粒数的相关性比视觉FDK%更好。该技术可以成功地用于非破坏性地分选具有镰刀菌损伤的籽粒，并估计这些籽粒的DON水平。单粒玉米的DON值较低（60 ppm），准确率约为96%。高DON籽粒的单粒DON水平可以用R-2 = 0.87和60.8ppm的预测标准误差（SEP）来估计。因为该方法是非破坏性的，种子可以被保存用于世代推进。自动化方法是快速的（1粒/秒），根据DON水平将谷物分选成几个部分，将为育种者提供比从散装种子样品中提供平均DON水平的技术更多的信息。
Fusarium Head Blight (FHB), or scab, can result in significant crop yield losses and contaminated grain in wheat (Triticum aestivum L.). Growing less susceptible cultivars is one of the most effective methods for managing FHB and for reducing deoxynivalenol (DON) levels in grain, but breeding programs lack a rapid and objective method for identifying the fungi and toxins. It is important to estimate proportions of sound kernels and Fusarium-damaged kernels (FDK) in grain and to estimate DON levels of FDK to objectively assess the resistance of a cultivar. An automated single kernel near-infrared (SKNIR) spectroscopic method for identification of FDK and for estimating DON levels was evaluated. The SKNIR system classified visually sound and FDK with an accuracy of 98.8 and 99.9%, respectively. The sound fraction had no or very little accumulation of DON. The FDK fraction was sorted into fractions with high or low DON content. The kernels identified as FDK by the SKNIR system had better correlation with other FHB assessment indices such as FHB severity, FHB incidence and kernels/g than visual FDK%. This technique can be successfully employed to nondestructively sort kernels with Fusarium damage and to estimate DON levels of those kernels. Single kernels could be predicted as having low (60 ppm) DON with approximate to 96%accuracy. Single kernel DON levels of the high DON kernels could be estimated with R-2 = 0.87 and standard error of prediction (SEP) of 60.8 ppm. Because the method is nondestructive, seeds may be saved for generation advancement. The automated method is rapid (1 kernel/sec) and sorting grains into several fractions depending on DON levels will provide breeders with more information than techniques that deliver average DON levels from bulk seed samples.