National Yang-Ming University, Biophotonics and Molecular Imaging Research Center, Taipei, 11221, R.O.C., Taiwan.
National Taiwan University, Graduate Institute of Photonics and Optoelectronics, Taipei, 10617, R.O.C., Taiwan.
Sci Rep. 2019 Feb 4;9(1):1167. doi: 10.1038/s41598-018-38165-3.
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.
叶片衰老为研究植物器官水平的年龄相关程序化降解提供了一个独特的窗口。本研究利用光谱域光相干断层扫描(SD-OCT)技术研究了落叶植物 Acer serrulatum Hayata 叶片衰老过程中的体内微观结构变化。Hayata 叶片具有秋季物候特征,会从绿色变为黄色,最后变为红色。SD-OCT 图像分析显示不同叶片层之间具有独特的特征;与绿色叶片相比,红色叶片的上表皮和栅栏组织层融合形成更厚的层。此外,A 扫描分析显示,从绿色到红色叶片,衰减系数(波长范围:1100-1550nm)显著降低(p<0.001)。此外,B 扫描分析还显示,从二阶空间灰度共生矩阵(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,确定了一组三个特征(能量、偏度和和方差),能够定量区分三种不同颜色叶片的微观结构差异。此外,基于 k-最近邻算法(k-NN)的分类发现,其具有 98%的灵敏度、99%的特异性和 95.5%的准确率。该技术为作物管理中的质量控制提供了一种便携式非侵入性工具。