Near-infrared spectroscopy (NIRS) predicts non-structural carbohydrate concentrations in different tissue types of a broad range of tree species

The allocation of non-structural carbohydrates (NSCs) to reserves constitutes an important physiological mechanism associated with tree growth and survival. However, procedures for measuring NSC in plant tissue are expensive and time-consuming. Near-infrared spectroscopy (NIRS) is a high-throughput...

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Tipo de recurso:
Fecha de publicación:
2015
Institución:
Universidad del Rosario
Repositorio:
Repositorio EdocUR - U. Rosario
Idioma:
eng
OAI Identifier:
oai:repository.urosario.edu.co:10336/22591
Acceso en línea:
https://doi.org/10.1111/2041-210X.12391
https://repository.urosario.edu.co/handle/10336/22591
Palabra clave:
Carbohydrate reserves
Carbon allocation
CARS-PLSR
Multivariate calibration
Spectroscopy
Starch
Sugars
Rights
License
Abierto (Texto Completo)
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network_name_str Repositorio EdocUR - U. Rosario
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spelling a96d9b86-040b-4d84-bf49-93f8d9cc09fb8041617760055c0cee3-186d-4fac-b9b4-f92e7fa6f7be86419a41-4cb5-485c-92b0-9db958c0ecb728eb26f0-45ea-4ad6-8acd-7c37217c28103496ded7-fb3a-4d8d-affa-7ed3f925970ffc8aa420-5591-479d-a94c-ce36b712d94c2020-05-25T23:57:03Z2020-05-25T23:57:03Z2015The allocation of non-structural carbohydrates (NSCs) to reserves constitutes an important physiological mechanism associated with tree growth and survival. However, procedures for measuring NSC in plant tissue are expensive and time-consuming. Near-infrared spectroscopy (NIRS) is a high-throughput technology that has the potential to infer the concentration of organic constituents for a large number of samples in a rapid and inexpensive way based on empirical calibrations with chemical analysis. The main objectives of this study were (i) to develop a general NSC concentration calibration that integrates various forms of variation such as tree species and tissue types and (ii) to identify characteristic spectral regions associated with NSC molecules. In total, 180 samples from different tree organs (root, stem, branch, leaf) belonging to 73 tree species from tropical and temperate biomes were analysed. Statistical relationships between NSC concentration and NIRS spectra were assessed using partial least squares regression (PLSR) and a variable selection procedure (competitive adaptive reweighted sampling, CARS), in order to identify key wavelengths. Parsimonious and accurate calibration models were obtained for total NSC (r2 of 0·91, RMSE of 1·34% in external validation), followed by starch (r2 = 0·85 and RMSE = 1·20%) and sugars (r2 = 0·82 and RMSE = 1·10%). Key wavelengths coincided among these models and were mainly located in the 1740-1800, 2100-2300 and 2410-2490 nm spectral regions. This study demonstrates the ability of general calibration model to infer NSC concentrations across species and tissue types in a rapid and cost-effective way. The estimation of NSC in plants using NIRS therefore serves as a tool for functional biodiversity research, in particular for the study of the growth-survival trade-off and its implications in response to changing environmental conditions, including growth limitation and mortality. © 2015 British Ecological Society.application/pdfhttps://doi.org/10.1111/2041-210X.123912041210Xhttps://repository.urosario.edu.co/handle/10336/22591engBritish Ecological Society1025No. 91018Methods in Ecology and EvolutionVol. 6Methods in Ecology and Evolution, ISSN:2041210X, Vol.6, No.9 (2015); pp. 1018-1025https://www.scopus.com/inward/record.uri?eid=2-s2.0-84941763635&doi=10.1111%2f2041-210X.12391&partnerID=40&md5=f16a70c32ffac83e37b5732df43d5d15Abierto (Texto Completo)http://purl.org/coar/access_right/c_abf2instname:Universidad del Rosarioreponame:Repositorio Institucional EdocURCarbohydrate reservesCarbon allocationCARS-PLSRMultivariate calibrationSpectroscopyStarchSugarsNear-infrared spectroscopy (NIRS) predicts non-structural carbohydrate concentrations in different tissue types of a broad range of tree speciesarticleArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501Ramirez, Jorge A.Posada Hostettler, Juan Manuel RobertoHanda, I. TanyaHoch, GünterVohland, MichaelMessier, ChristianReu, Björn10336/22591oai:repository.urosario.edu.co:10336/225912022-05-02 07:37:16.29022https://repository.urosario.edu.coRepositorio institucional EdocURedocur@urosario.edu.co
dc.title.spa.fl_str_mv Near-infrared spectroscopy (NIRS) predicts non-structural carbohydrate concentrations in different tissue types of a broad range of tree species
title Near-infrared spectroscopy (NIRS) predicts non-structural carbohydrate concentrations in different tissue types of a broad range of tree species
spellingShingle Near-infrared spectroscopy (NIRS) predicts non-structural carbohydrate concentrations in different tissue types of a broad range of tree species
Carbohydrate reserves
Carbon allocation
CARS-PLSR
Multivariate calibration
Spectroscopy
Starch
Sugars
title_short Near-infrared spectroscopy (NIRS) predicts non-structural carbohydrate concentrations in different tissue types of a broad range of tree species
title_full Near-infrared spectroscopy (NIRS) predicts non-structural carbohydrate concentrations in different tissue types of a broad range of tree species
title_fullStr Near-infrared spectroscopy (NIRS) predicts non-structural carbohydrate concentrations in different tissue types of a broad range of tree species
title_full_unstemmed Near-infrared spectroscopy (NIRS) predicts non-structural carbohydrate concentrations in different tissue types of a broad range of tree species
title_sort Near-infrared spectroscopy (NIRS) predicts non-structural carbohydrate concentrations in different tissue types of a broad range of tree species
dc.subject.keyword.spa.fl_str_mv Carbohydrate reserves
Carbon allocation
CARS-PLSR
Multivariate calibration
Spectroscopy
Starch
Sugars
topic Carbohydrate reserves
Carbon allocation
CARS-PLSR
Multivariate calibration
Spectroscopy
Starch
Sugars
description The allocation of non-structural carbohydrates (NSCs) to reserves constitutes an important physiological mechanism associated with tree growth and survival. However, procedures for measuring NSC in plant tissue are expensive and time-consuming. Near-infrared spectroscopy (NIRS) is a high-throughput technology that has the potential to infer the concentration of organic constituents for a large number of samples in a rapid and inexpensive way based on empirical calibrations with chemical analysis. The main objectives of this study were (i) to develop a general NSC concentration calibration that integrates various forms of variation such as tree species and tissue types and (ii) to identify characteristic spectral regions associated with NSC molecules. In total, 180 samples from different tree organs (root, stem, branch, leaf) belonging to 73 tree species from tropical and temperate biomes were analysed. Statistical relationships between NSC concentration and NIRS spectra were assessed using partial least squares regression (PLSR) and a variable selection procedure (competitive adaptive reweighted sampling, CARS), in order to identify key wavelengths. Parsimonious and accurate calibration models were obtained for total NSC (r2 of 0·91, RMSE of 1·34% in external validation), followed by starch (r2 = 0·85 and RMSE = 1·20%) and sugars (r2 = 0·82 and RMSE = 1·10%). Key wavelengths coincided among these models and were mainly located in the 1740-1800, 2100-2300 and 2410-2490 nm spectral regions. This study demonstrates the ability of general calibration model to infer NSC concentrations across species and tissue types in a rapid and cost-effective way. The estimation of NSC in plants using NIRS therefore serves as a tool for functional biodiversity research, in particular for the study of the growth-survival trade-off and its implications in response to changing environmental conditions, including growth limitation and mortality. © 2015 British Ecological Society.
publishDate 2015
dc.date.created.spa.fl_str_mv 2015
dc.date.accessioned.none.fl_str_mv 2020-05-25T23:57:03Z
dc.date.available.none.fl_str_mv 2020-05-25T23:57:03Z
dc.type.eng.fl_str_mv article
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.spa.spa.fl_str_mv Artículo
dc.identifier.doi.none.fl_str_mv https://doi.org/10.1111/2041-210X.12391
dc.identifier.issn.none.fl_str_mv 2041210X
dc.identifier.uri.none.fl_str_mv https://repository.urosario.edu.co/handle/10336/22591
url https://doi.org/10.1111/2041-210X.12391
https://repository.urosario.edu.co/handle/10336/22591
identifier_str_mv 2041210X
dc.language.iso.spa.fl_str_mv eng
language eng
dc.relation.citationEndPage.none.fl_str_mv 1025
dc.relation.citationIssue.none.fl_str_mv No. 9
dc.relation.citationStartPage.none.fl_str_mv 1018
dc.relation.citationTitle.none.fl_str_mv Methods in Ecology and Evolution
dc.relation.citationVolume.none.fl_str_mv Vol. 6
dc.relation.ispartof.spa.fl_str_mv Methods in Ecology and Evolution, ISSN:2041210X, Vol.6, No.9 (2015); pp. 1018-1025
dc.relation.uri.spa.fl_str_mv https://www.scopus.com/inward/record.uri?eid=2-s2.0-84941763635&doi=10.1111%2f2041-210X.12391&partnerID=40&md5=f16a70c32ffac83e37b5732df43d5d15
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.acceso.spa.fl_str_mv Abierto (Texto Completo)
rights_invalid_str_mv Abierto (Texto Completo)
http://purl.org/coar/access_right/c_abf2
dc.format.mimetype.none.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv British Ecological Society
institution Universidad del Rosario
dc.source.instname.spa.fl_str_mv instname:Universidad del Rosario
dc.source.reponame.spa.fl_str_mv reponame:Repositorio Institucional EdocUR
repository.name.fl_str_mv Repositorio institucional EdocUR
repository.mail.fl_str_mv edocur@urosario.edu.co
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