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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...

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Autores principales: Villesseche, Héloïse, Ecarnot, Martin, Ballini, Elsa, Bendoula, Ryad, Gorretta, Nathalie, Roumet, Pierre
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
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.
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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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