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Elucidation of microstructural changes in leaves during senescence using spectral domain optical coherence tomography

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. Haya...

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Autores principales: Anna, Tulsi, Chakraborty, Sandeep, Cheng, Chia-Yi, Srivastava, Vishal, Chiou, Arthur, Kuo, Wen-Chuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6362184/
https://www.ncbi.nlm.nih.gov/pubmed/30718740
http://dx.doi.org/10.1038/s41598-018-38165-3
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author Anna, Tulsi
Chakraborty, Sandeep
Cheng, Chia-Yi
Srivastava, Vishal
Chiou, Arthur
Kuo, Wen-Chuan
author_facet Anna, Tulsi
Chakraborty, Sandeep
Cheng, Chia-Yi
Srivastava, Vishal
Chiou, Arthur
Kuo, Wen-Chuan
author_sort Anna, Tulsi
collection PubMed
description 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.
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spelling pubmed-63621842019-02-06 Elucidation of microstructural changes in leaves during senescence using spectral domain optical coherence tomography Anna, Tulsi Chakraborty, Sandeep Cheng, Chia-Yi Srivastava, Vishal Chiou, Arthur Kuo, Wen-Chuan Sci Rep Article 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. Nature Publishing Group UK 2019-02-04 /pmc/articles/PMC6362184/ /pubmed/30718740 http://dx.doi.org/10.1038/s41598-018-38165-3 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Anna, Tulsi
Chakraborty, Sandeep
Cheng, Chia-Yi
Srivastava, Vishal
Chiou, Arthur
Kuo, Wen-Chuan
Elucidation of microstructural changes in leaves during senescence using spectral domain optical coherence tomography
title Elucidation of microstructural changes in leaves during senescence using spectral domain optical coherence tomography
title_full Elucidation of microstructural changes in leaves during senescence using spectral domain optical coherence tomography
title_fullStr Elucidation of microstructural changes in leaves during senescence using spectral domain optical coherence tomography
title_full_unstemmed Elucidation of microstructural changes in leaves during senescence using spectral domain optical coherence tomography
title_short Elucidation of microstructural changes in leaves during senescence using spectral domain optical coherence tomography
title_sort elucidation of microstructural changes in leaves during senescence using spectral domain optical coherence tomography
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6362184/
https://www.ncbi.nlm.nih.gov/pubmed/30718740
http://dx.doi.org/10.1038/s41598-018-38165-3
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