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Unsupervised analysis of NIRS spectra to assess complex plant traits: leaf senescence as a use case
BACKGROUND: As a rapid and non-destructive method, Near Infrared Spectroscopy is classically proposed to assess plant traits in many scientific fields, to observe enlarged genotype panels and to document the temporal kinetic of some biological processes. Most often, supervised models are used. The s...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9373489/ https://www.ncbi.nlm.nih.gov/pubmed/35962438 http://dx.doi.org/10.1186/s13007-022-00927-6 |
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author | Villesseche, Héloïse Ecarnot, Martin Ballini, Elsa Bendoula, Ryad Gorretta, Nathalie Roumet, Pierre |
author_facet | Villesseche, Héloïse Ecarnot, Martin Ballini, Elsa Bendoula, Ryad Gorretta, Nathalie Roumet, Pierre |
author_sort | Villesseche, Héloïse |
collection | PubMed |
description | BACKGROUND: As a rapid and non-destructive method, Near Infrared Spectroscopy is classically proposed to assess plant traits in many scientific fields, to observe enlarged genotype panels and to document the temporal kinetic of some biological processes. Most often, supervised models are used. The signal is calibrated thanks to reference measurements, and dedicated models are generated to predict biological traits. An alternative unsupervised approach considers the whole spectra information in order to point out various matrix changes. Although more generic, and faster to implement, as it does not require a reference data set, this latter approach is rarely used to document biological processes, and does requires more information of the process. METHODS: In our work, an unsupervised model was used to document the flag leaf senescence of durum wheat (Triticum turgidum durum). Leaf spectra changes were observed using Moving Window Principal Component Analysis (MWPCA). The dates related to earlier and later spectra changes were compared to two key points on the senescence time course: senescence onset (T0) and the end of the leaf span (T1) derived from a supervised strategy. RESULTS: For almost all leaves and whatever the signal pre-treatments and window size considered, the MWPCA found significant spectral changes. The latter was highly correlated with T1 (0.59 ≤ r ≤ 0.86) whereas the correlations between the first significant spectrum changes and T0 were lower (0.09 ≤ r ≤ 0.56). These different relationships are discussed below since they define the potential as well as the limitations of MWPCA to model biological processes. CONCLUSION: Overall, our study demonstrates that the information contained in the spectra can be used when applying an unsupervised method, here the MWPCA, to characterize a complex biological phenomenon such leaf senescence. It also means that using whole spectra may be relevant in agriculture and plant biology. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13007-022-00927-6. |
format | Online Article Text |
id | pubmed-9373489 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-93734892022-08-13 Unsupervised analysis of NIRS spectra to assess complex plant traits: leaf senescence as a use case Villesseche, Héloïse Ecarnot, Martin Ballini, Elsa Bendoula, Ryad Gorretta, Nathalie Roumet, Pierre Plant Methods Research BACKGROUND: As a rapid and non-destructive method, Near Infrared Spectroscopy is classically proposed to assess plant traits in many scientific fields, to observe enlarged genotype panels and to document the temporal kinetic of some biological processes. Most often, supervised models are used. The signal is calibrated thanks to reference measurements, and dedicated models are generated to predict biological traits. An alternative unsupervised approach considers the whole spectra information in order to point out various matrix changes. Although more generic, and faster to implement, as it does not require a reference data set, this latter approach is rarely used to document biological processes, and does requires more information of the process. METHODS: In our work, an unsupervised model was used to document the flag leaf senescence of durum wheat (Triticum turgidum durum). Leaf spectra changes were observed using Moving Window Principal Component Analysis (MWPCA). The dates related to earlier and later spectra changes were compared to two key points on the senescence time course: senescence onset (T0) and the end of the leaf span (T1) derived from a supervised strategy. RESULTS: For almost all leaves and whatever the signal pre-treatments and window size considered, the MWPCA found significant spectral changes. The latter was highly correlated with T1 (0.59 ≤ r ≤ 0.86) whereas the correlations between the first significant spectrum changes and T0 were lower (0.09 ≤ r ≤ 0.56). These different relationships are discussed below since they define the potential as well as the limitations of MWPCA to model biological processes. CONCLUSION: Overall, our study demonstrates that the information contained in the spectra can be used when applying an unsupervised method, here the MWPCA, to characterize a complex biological phenomenon such leaf senescence. It also means that using whole spectra may be relevant in agriculture and plant biology. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13007-022-00927-6. BioMed Central 2022-08-12 /pmc/articles/PMC9373489/ /pubmed/35962438 http://dx.doi.org/10.1186/s13007-022-00927-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Villesseche, Héloïse Ecarnot, Martin Ballini, Elsa Bendoula, Ryad Gorretta, Nathalie Roumet, Pierre Unsupervised analysis of NIRS spectra to assess complex plant traits: leaf senescence as a use case |
title | Unsupervised analysis of NIRS spectra to assess complex plant traits: leaf senescence as a use case |
title_full | Unsupervised analysis of NIRS spectra to assess complex plant traits: leaf senescence as a use case |
title_fullStr | Unsupervised analysis of NIRS spectra to assess complex plant traits: leaf senescence as a use case |
title_full_unstemmed | Unsupervised analysis of NIRS spectra to assess complex plant traits: leaf senescence as a use case |
title_short | Unsupervised analysis of NIRS spectra to assess complex plant traits: leaf senescence as a use case |
title_sort | unsupervised analysis of nirs spectra to assess complex plant traits: leaf senescence as a use case |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9373489/ https://www.ncbi.nlm.nih.gov/pubmed/35962438 http://dx.doi.org/10.1186/s13007-022-00927-6 |
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