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首页> 外文期刊>Biosystems Engineering >Non-destructive identification of maize haploid seeds using nonlinear analysis method based on their near-infrared spectra
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Non-destructive identification of maize haploid seeds using nonlinear analysis method based on their near-infrared spectra

机译：基于近红外光谱的非线性分析方法，非线性分析方法的非破坏性鉴定玉米单倍体种子

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摘要

The identification of maize haploid seeds is a significant process in genetic research and modern maize breeding. Adopting near-infrared spectroscopy technology to distinguish haploid seeds from hybrid seeds has the advantages of being non-destructive, rapid and low cost. However, due to the influence of light, temperature, humidity, near-infrared intensity, instrument and dynamic change of seed activity, the near-infrared spectra of maize seeds showed high dimensional nonlinear characteristics. In this study, to make full use of the class label information, a nonlinear feature analytical method for haploid maize seeds identification based on Supervised Virtual Sample Kernel Locality Preserving Projection (SVSKLPP) has been proposed. The experimental results showed that the nonlinear identification model SVSKLPP achieved strong classification performance to identify the maize haploid seeds. Moreover, compared with the linear feature extraction methods Principal Component Analysis, Orthogonal Linear Discriminant Analysis, Locality Preserving Projection, Supervised Virtual Sample Locality Preserving Projection and nonlinear feature extraction methods Kernel Locality Preserving Projection, Isomap, Locally Linear Embedding, Laplacian Eigenmaps and Local Tangent Space Alignment, the SVSKLPP model achieved a better performance. The average accuracy, sensitivity and specificity using method SVSKLPP were 97.1%, 98.8% and 95.4% respectively, and it also had high robustness. The overall results show that the SVSKLPP-NIR methodology was efficient in accurately identifying haploid maize seeds, thus demonstrating its capabilities for application in haploid breeding for crop variety improvement. (C) 2018 IAgrE. Published by Elsevier Ltd. All rights reserved.
机译：玉米单倍体种子的鉴定是遗传研究和现代玉米育种的重要过程。采用近红外光谱技术，以区分单倍体种子从混合种子中具有非破坏性，快速和低成本的优点。然而，由于光，温度，湿度，近红外强度，仪器和种子活性的动态变化的影响，玉米种子的近红外光谱显示出高尺寸非线性特性。在本研究中，为了充分利用类标签信息，已经提出了一种基于监督虚拟样本内核位置保留投影（SVSKLPP）的单倍体玉米种子识别的非线性特征分析方法。实验结果表明，非线性识别模型SVSKLPP达到了强烈的分类性能，以鉴定玉米单倍体种子。此外，与线性特征提取方法进行比较主成分分析，正交线性判别分析，定位保存投影，监督虚拟样本局部定位预测投影和非线性特征提取方法核心标准保存投影，ISOMAP，局部线性嵌入，LAPLACIAN EIGENMAPS和局部切割空间对齐，SVSKLPP模型实现了更好的性能。使用方法SVSKLPP的平均精度，敏感性和特异性分别为97.1％，98.8％和95.4％，它也具有高稳健性。总体结果表明，SVSKLPP-NIR方法在准确识别单倍体玉米种子方面有效，从而证明其在单倍体育种中的应用程序适用于作物品种改善。 （c）2018年IAGRE。 elsevier有限公司出版。保留所有权利。

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