High-Resolution and Non-destructive Evaluation of the Spatial Distribution of Nitrate and Its Dynamics in Spinach (Spinacia oleracea L.) Leaves by Near-Infrared Hyperspectral Imaging
Nitrate is an important component of the nitrogen cycle and is therefore present in all plants. However, excessive nitrogen fertilization results in a high nitrate content in vegetables, which is unhealthy for humans. Understanding the spatial distribution of nitrate in leaves is beneficial for impr...
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doaj-0cbba8e489bd4dc394d2af2b993465fb2020-11-25T00:52:42ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2017-11-01810.3389/fpls.2017.01937293935High-Resolution and Non-destructive Evaluation of the Spatial Distribution of Nitrate and Its Dynamics in Spinach (Spinacia oleracea L.) Leaves by Near-Infrared Hyperspectral ImagingHao-Yu Yang0Hao-Yu Yang1Tetsuya Inagaki2Te Ma3Satoru Tsuchikawa4Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, ChinaGraduate School of Bioagricultural Sciences, Nagoya University, Nagoya, JapanGraduate School of Bioagricultural Sciences, Nagoya University, Nagoya, JapanGraduate School of Bioagricultural Sciences, Nagoya University, Nagoya, JapanGraduate School of Bioagricultural Sciences, Nagoya University, Nagoya, JapanNitrate is an important component of the nitrogen cycle and is therefore present in all plants. However, excessive nitrogen fertilization results in a high nitrate content in vegetables, which is unhealthy for humans. Understanding the spatial distribution of nitrate in leaves is beneficial for improving nitrogen assimilation efficiency and reducing its content in vegetables. In this study, near-infrared (NIR) hyperspectral imaging was used for the non-destructive and effective evaluation of nitrate content in spinach (Spinacia oleracea L.) leaves. Leaf samples with different nitrate contents were collected under various fertilization conditions, and reference data were obtained using reflectometer apparatus RQflex 10. Partial least squares regression analysis revealed that there was a high correlation between the reference data and NIR spectra (r2 = 0.74, root mean squared error of cross-validation = 710.16 mg/kg). Furthermore, the nitrate content in spinach leaves was successfully mapped at a high spatial resolution, clearly displaying its distribution in the petiole, vein, and blade. Finally, the mapping results demonstrated dynamic changes in the nitrate content in intact leaf samples under different storage conditions, showing the value of this non-destructive tool for future analyses of the nitrate content in vegetables.http://journal.frontiersin.org/article/10.3389/fpls.2017.01937/fullnitrate contentnear-infrared hyperspectral imagingpartial least squaresvegetable |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hao-Yu Yang Hao-Yu Yang Tetsuya Inagaki Te Ma Satoru Tsuchikawa |
spellingShingle |
Hao-Yu Yang Hao-Yu Yang Tetsuya Inagaki Te Ma Satoru Tsuchikawa High-Resolution and Non-destructive Evaluation of the Spatial Distribution of Nitrate and Its Dynamics in Spinach (Spinacia oleracea L.) Leaves by Near-Infrared Hyperspectral Imaging Frontiers in Plant Science nitrate content near-infrared hyperspectral imaging partial least squares vegetable |
author_facet |
Hao-Yu Yang Hao-Yu Yang Tetsuya Inagaki Te Ma Satoru Tsuchikawa |
author_sort |
Hao-Yu Yang |
title |
High-Resolution and Non-destructive Evaluation of the Spatial Distribution of Nitrate and Its Dynamics in Spinach (Spinacia oleracea L.) Leaves by Near-Infrared Hyperspectral Imaging |
title_short |
High-Resolution and Non-destructive Evaluation of the Spatial Distribution of Nitrate and Its Dynamics in Spinach (Spinacia oleracea L.) Leaves by Near-Infrared Hyperspectral Imaging |
title_full |
High-Resolution and Non-destructive Evaluation of the Spatial Distribution of Nitrate and Its Dynamics in Spinach (Spinacia oleracea L.) Leaves by Near-Infrared Hyperspectral Imaging |
title_fullStr |
High-Resolution and Non-destructive Evaluation of the Spatial Distribution of Nitrate and Its Dynamics in Spinach (Spinacia oleracea L.) Leaves by Near-Infrared Hyperspectral Imaging |
title_full_unstemmed |
High-Resolution and Non-destructive Evaluation of the Spatial Distribution of Nitrate and Its Dynamics in Spinach (Spinacia oleracea L.) Leaves by Near-Infrared Hyperspectral Imaging |
title_sort |
high-resolution and non-destructive evaluation of the spatial distribution of nitrate and its dynamics in spinach (spinacia oleracea l.) leaves by near-infrared hyperspectral imaging |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Plant Science |
issn |
1664-462X |
publishDate |
2017-11-01 |
description |
Nitrate is an important component of the nitrogen cycle and is therefore present in all plants. However, excessive nitrogen fertilization results in a high nitrate content in vegetables, which is unhealthy for humans. Understanding the spatial distribution of nitrate in leaves is beneficial for improving nitrogen assimilation efficiency and reducing its content in vegetables. In this study, near-infrared (NIR) hyperspectral imaging was used for the non-destructive and effective evaluation of nitrate content in spinach (Spinacia oleracea L.) leaves. Leaf samples with different nitrate contents were collected under various fertilization conditions, and reference data were obtained using reflectometer apparatus RQflex 10. Partial least squares regression analysis revealed that there was a high correlation between the reference data and NIR spectra (r2 = 0.74, root mean squared error of cross-validation = 710.16 mg/kg). Furthermore, the nitrate content in spinach leaves was successfully mapped at a high spatial resolution, clearly displaying its distribution in the petiole, vein, and blade. Finally, the mapping results demonstrated dynamic changes in the nitrate content in intact leaf samples under different storage conditions, showing the value of this non-destructive tool for future analyses of the nitrate content in vegetables. |
topic |
nitrate content near-infrared hyperspectral imaging partial least squares vegetable |
url |
http://journal.frontiersin.org/article/10.3389/fpls.2017.01937/full |
work_keys_str_mv |
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