Deoxynivalenol screening in wheat kernels using hyperspectral imaging
Deoxynivalenol screening in wheat kernels using hyperspectral imaging
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DOI：
10.1016/j.biosystemseng.2016.12.004
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发表时间：
2017-03-01
影响因子：
5.1
通讯作者：
Pontes Lima, Maria Irnaculada
中科院分区：
文献类型：
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作者：
Arnal Barbedo, Jayme Garcia;Tibola, Casiane Salete;Pontes Lima, Maria Irnaculada
利用高光谱成像技术对小麦籽粒中脱氧雪腐镰刀菌烯醇（DON）进行了检测。实验进行了使用一种新的算法，设计是简单的实施和计算轻，主要是基于几个选定的光谱带的操纵。初步实验结果表明，直接估计DON含量使用高光谱图像是目前不可行的，但他们也表明，间接分析探索镰刀菌损害和DON含量之间的相关性可能是足够准确的，以改善DON筛选过程中的生产链。这促使采用分类方法，其中算法不是估计DON含量的值，而是根据应用将小麦籽粒批次分为两个或三个类别。所开发的算法实现了72%和81%的三个和两个类的分类方案，分别准确率。结果虽然不足以提供结论性筛选，但表明该算法可用于初始筛选以检测需要进一步分析其DON含量的小麦批次。(C)2016年IAgRE。由爱思唯尔有限公司出版。保留所有权利。
The use of hyperspectral imaging (HSI) for deoxynivalenol (DON) screening in wheat kernels is investigated. Experiments were carried out using a new algorithm designed to be simple to implement and computationally light, being largely based on the manipulation of a few selected spectral bands. Initial experimental results revealed that direct estimation of DON content using hyperspectral images is currently unfeasible, but they also indicated that an indirect analysis exploring the correlation between Fusarium damage and DON content may be accurate enough to improve the process of DON screening in the production chain. This motivated the adoption of a classification approach, in which an algorithm, instead of estimating a value for the DON content, classifies wheat kernel batches into two or three categories, depending on the application. The developed algorithm achieved accuracies of 72% and 81% for the three-and two-class classification schemes, respectively. The results, although not accurate enough to provide conclusive screening, indicated that the algorithm could be used for initial screening to detect wheat batches that warrant further analysis regarding their DON content. (C) 2016 IAgrE. Published by Elsevier Ltd. All rights reserved.