169
Views
0
CrossRef citations to date
0
Altmetric
Research Articles

Multispecies, multisite, multi-age PLS regression models of chemical properties of eucalypts wood using Fourier Transformed near-Infrared (FT-NIR) spectroscopy

ORCID Icon, ORCID Icon, , ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon show all
Pages 419-434 | Published online: 02 Sep 2022
 

Abstract

Near Infrared Spectroscopy (NIR) is often used to perform high throughput phenotyping on thousands of genotypes using prediction models with high variability. A study was therefore undertaken to analyze the potential of multispecies, multisite and multi-age NIR calibration models of seven chemical properties of eucalyptus wood. The models are based on 358 samples selected among more than 5000 samples that belong to five eucalypt species including hybrids. The samples were collected from trees aged 2-35 originating from four different countries. Spectra were measured on non-extracted wood powders using an FT-NIR spectrometer. Models were established in the spectral range of 9090-4040 cm−1 using the PLS regression method, tested by repeated cross-validation and validated on independent test sets. The results showed that the robust models for total extractives (R2P = 0.91, RMSEP = 1.20%, RPD = 3.3) and KL (R2P = 0.89, RMSEP = 1.21%, RPD = 3.0) provided good predictions. These two properties were the best predicted, followed by the S/G ratio (R2P = 0.84, RMSEP = 0.19, RPD = 2.5) and ASL content (R2P = 0.81, RMSEP of 0.54, RPD = 2.3). For holocellulose, alphacellulose, and hemicelluloses contents, the models provided approximate predictions. The prediction errors were always less than twice of the laboratory errors except for ASL and S/G ratio. For total extractives and ASL, β-coefficients of models were of approximately the same magnitude throughout the 9000-4000 cm−1 region while for the five other properties, they were higher in the 7500-4000 cm−1 region. Models were also established in narrower NIR regions, and the quality of models obtained was about the same as that of the models based in the 9090-4000 cm−1 wide range. These established robust models can be used to make predictions based on samples of high variability.

Acknowledgements

The data on chemical properties used in this study were obtained in several research projects including the Agropolis Foundation - CAPES project, Massamba project, Cenibra project, Loudima_73 project, Plantar project, BRG, Tree for Joule project, WUETREE project, R02_02 project, R90_13 project and R91_10 project. The authors are grateful to INRA Orléans for the R script used to establish NIR models, and to the « Plateau de Phénotypage Biochimique » of the AGAP UMR of CIRAD on which most of the laboratory analysis was carried out. The data analysis and field sample collection work were supported by CIRAD's « Actions Incitatives », by the « DP-forêts et biodiversité » and by the YSI (Young Scientist Initiative) award from IUFRO-EFI (International Union of Forest Research Organizations - European Forest Institute). This study is part of a PhD thesis carried out within the framework of the SPIRMADBOIS project funded by the « Agence Universitaire pour la Francophonie (AUF) », and the G3D project « Gestion Durable des bois précieux Dalbergia et Diospyros de Madagascar, appui scientifique à la mise en oeuvre du plan d’action de la CITES » funded by the Delegation of the European Union to Madagascar.

Disclosure statement

No potential conflict of interest was reported by the authors.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 919.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.