Elucidation of microstructural changes in leaves during senescence using spectral domain optical coherence tomography
Elucidation of microstructural changes in leaves during senescence using spectral domain optical coherence tomography
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DOI：
10.1038/s41598-018-38165-3
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发表时间：
2019-02-04
影响因子：
4.6
通讯作者：
Kuo, Wen-Chuan
中科院分区：
文献类型：
--
作者：
Anna, Tulsi;Chakraborty, Sandeep;Kuo, Wen-Chuan
叶片衰老为研究植物器官衰老过程提供了一个独特的窗口。在这里，光谱域光学相干断层扫描（SD-OCT）已被用来研究在体内衰老的落叶植物细齿槭叶微结构的变化。早田的叶子显示出秋天的物候，颜色从绿色变成黄色，最后变成红色。SD-OCT图像分析显示不同层次的叶片之间的独特特征;上表皮和栅栏层的合并形成厚层相比，绿色叶的红色叶片。此外，A-扫描分析显示从绿色到红色叶片的衰减系数（对于波长范围：1100-1550 nm）显著（p < 0.001）降低。此外，B扫描分析还发现，从二阶空间灰度依赖矩阵（SGLDM）中提取的14个纹理参数也发生了显著变化。在这些参数中，一组三个功能（能量，偏度，和方差之和），能够定量区分三种不同颜色的叶片的微观结构的差异，已被确定。此外，发现基于k-最近邻算法（k-NN）的分类产生98%的灵敏度，99%的特异性和95.5%的准确性。根据所提出的技术，可以预期一种用于作物管理质量控制的便携式非侵入性工具。
Leaf senescence provides a unique window to explore the age-dependent programmed degradation at organ label in plants. Here, spectral domain optical coherence tomography (SD-OCT) has been used to study in vivo senescing leaf microstructural changes in the deciduous plant Acer serrulatum Hayata. Hayata leaves show autumn phenology and change color from green to yellow and finally red. SD-OCT image analysis shows distinctive features among different layers of the leaves; merging of upper epidermis and palisade layers form thicker layers in red leaves compared to green leaves. Moreover, A-scan analysis showed a significant (p < 0.001) decrease in the attenuation coefficient (for wavelength range: 1100-1550 nm) from green to red leaves. In addition, the B-scan analysis also showed significant changes in 14 texture parameters extracted from second-order spatial gray level dependence matrix (SGLDM). Among these parameters, a set of three features (energy, skewness, and sum variance), capable of quantitatively distinguishing difference in the microstructures of three different colored leaves, has been identified. Furthermore, classification based on k-nearest neighbors algorithm (k-NN) was found to yield 98% sensitivity, 99% specificity, and 95.5% accuracy. Following the proposed technique, a portable noninvasive tool for quality control in crop management can be anticipated.