NIR-based models for estimating selected physical and chemical wood properties from fast-growing plantations

doi:10.3832/ifor4030-015

NIR-based models for estimating selected physical and chemical wood properties from fast-growing plantations

Breno Assis Loureiro, Taiana Guimaraes Arriel , Fernanda Maria Guedes Ramalho, Paulo Ricardo Gherardi Hein, Paulo Fernando Trugilho

iForest - Biogeosciences and Forestry, Volume 15, Issue 5, Pages 372-380 (2022)
doi: https://doi.org/10.3832/ifor4030-015
Published: Oct 05, 2022 - Copyright © 2022 SISEF

Research Articles

PDF Version

Full Text

Citation

Geo-mapping

As a faster, reliable, and low cost technique, applicable to large samplings, near infrared (NIR) spectroscopy technology has been widely applied for high-throughput phenotyping in forest breeding programmes. The aim of this study was to develop multivariate models for estimating the chemical and physical properties of juvenile wood based on NIR signatures of milled wood. Moreover, two approaches, namely, external validation by clone and by age, were tested to validate the model for estimating extractive content. NIR spectra of wood specimens taken from three clones of Eucalyptus urophylla (one to six years old) grown in southern Brazil were used to calibrate and validate models for predicting the wood basic density, total extractives, ash content, holocellulose content, syringyl to guaiacyl ratio (S/G) and elementary components of the wood. PLS-R models were validated by an independent set of wood specimens and presented promising statistics for the estimating wood density (R²p = 0.768), extractives (R²p = 0.912), ash (R²p = 0.936) and carbon (R²p = 0.697) contents from NIR signatures measured in the milled wood of young trees. Furthermore, NIR models for estimating the extractive content of wood were validated using the clones or ages left out of the training sets. Most models presented satisfactory statistics (R² > 90%) and could be applied to routine laboratory analyses or to select potential trees in Eucalyptus breeding programmes.

Keywords

Near Infrared, Wood Analysis, Predictive Models, Wood Powder, Eucalyptus, Multivariate Analysis

Authors’ address

(1)

0000-0002-2080-4765
0000-0002-3801-1878
0000-0001-5701-4479
0000-0002-9152-6803
0000-0002-6230-5462
Department of Forest Sciences, Federal University of Lavras, PO Box 3037, 37200-900 Lavras, MG (Brazil)

Corresponding author

Taiana Guimaraes Arriel
taianaarriel@hotmail.com

Citation

Assis Loureiro B, Arriel TG, Guedes Ramalho FM, Hein PRG, Trugilho PF (2022). NIR-based models for estimating selected physical and chemical wood properties from fast-growing plantations. iForest 15: 372-380. - doi: 10.3832/ifor4030-015

Academic Editor

Manuela Romagnoli

Paper history

Received: Dec 01, 2021
Accepted: Jul 21, 2022

First online: Oct 05, 2022
Publication Date: Oct 31, 2022
Publication Time: 2.53 months

Open Access

This article is distributed under the terms of the Creative Commons Attribution-Non Commercial 4.0 International (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Breakdown by View Type

(Waiting for server response...)

Article Usage

Total Article Views: 22487
(from publication date up to now)

Breakdown by View Type
HTML Page Views: 20354
Abstract Page Views: 1057
PDF Downloads: 825
Citation/Reference Downloads: 4
XML Downloads: 247

Web Metrics
Days since publication: 778
Overall contacts: 22487
Avg. contacts per week: 202.33

Article Citations

Article citations are based on data periodically collected from the Clarivate Web of Science web site
(last update: Feb 2023)

(No citations were found up to date. Please come back later)

(no Plumx Metrics available for this paper)

Publication Metrics

by Dimensions ^©

Articles citing this article

List of the papers citing this article based on CrossRef Cited-by.

(1)

ABNT (2003a)
NBR 11941: Madeira: determinação da densidade básica [Wood: basic density determination]. Brazilian Association of Technical Standards - ABTN, Rio de Janeiro, Brazil, pp. 6. [in Portuguese]
Gscholar

(2)

ABNT (2003b)
NBR 13999: Determinação do material inorgnico [Determination of inorganic material]. Brazilian Association of Technical Standards - ABTN, Rio de Janeiro, Brazil, pp. 4. [in Portuguese]
Gscholar

(3)

Arriel TG, Ramalho FMG, Lima RAB, Souza KIR, Hein PRG, Trugilho PF (2019)
Developing near infrared spectroscopic models for predicting density of Eucalyptus wood based on indirect measurement. Cerne 25 (3): 294-300.
CrossRef | Gscholar

(4)

ASTM (2013)
E870-82: standard test methods for analysis of wood fuels. American society for testing materials - ASTM, West Conshohocken, PA, USA, pp. 2.
Gscholar

(5)

Baillères H, Davrieux F, Ham-Pichavant F (2002)
Near infrared analysis as a tool for rapid screening of some major wood characteristics in a Eucalyptus breeding program. Annals of Forest Science 59: 479-790.
CrossRef | Gscholar

(6)

Brand MA, Muñiz GIB, Quirino WF, Brito JO (2011)
Storage as a tool to improve wood fuel quality. Biomass and Bioenergy 35 (7): 2581-2588.
CrossRef | Gscholar

(7)

Castro CAO, Nunes ACP, Roque JV, Teófilo RF, Santos OP, Santos GA, Gallo R, Pantuza IB, Resende MDV (2019)
Optimization of Eucalyptus benthamii progeny test based on Near-Infrared Spectroscopy approach and volumetric production. Industrial Crops and Products 141.
CrossRef | Gscholar

(8)

Chen JY, Matsunaga R, Ishikawa K, Zhang H (2003)
Main inorganic component measurement of seawater using near-infrared spectroscopy. Applied Spectroscopy 57: 1399-1406.
CrossRef | Gscholar

(9)

Costa LR, Trugilho PF, Hein PRG (2018)
Evaluation and classification of Eucalypt charcoal quality by near infrared spectroscopy. Biomass and Bioenergy 112: 85-92.
CrossRef | Gscholar

(10)

Demirbas A, Demirbas HA (2004)
Estimating the calorific values of lignocellulosic fuels. Energy, Exploration and Exploitation 1: 105-111.
CrossRef | Gscholar

(11)

Estopa RA, Milagres FR, Oliveira RA, Hein PRG (2017)
NIR spectroscopic models for phenotyping wood traits in breeding programs of Eucalyptus benthamii. Cerne 23 (3): 367-375.
CrossRef | Gscholar

(12)

FAO (2019)
FAOSTAT: forestry production and trade. Food and Agriculture Organization of the United Nations, Rome, Italy.
Online | Gscholar

(13)

Fujimoto T, Kurata Y, Matsumoto K, Tsuchikawa S (2008)
Application of near infrared spectroscopy for estimating wood mechanical properties of small clear and full lenght lumber specimens. Journal of Near Infrared Spectroscopy 6: 529-537.
CrossRef | Gscholar

(14)

Gouvêa AFG, Trugilho PF, Assis CO, Assis MR, Colodette JL, Gomes CM (2015)
Avaliação do efeito da relação siringila/guaiacila da lignina de eucalipto na produção de carvão vegetal [Evaluation of the effect of the syringyl/guaiacyl ratio of Eucalyptus lignin on charcoal production]. Ciência da Madeira 6: 71-78. [in Portuguese]
Gscholar

(15)

Hein PRG, Chaix G (2014)
NIR spectral heritability: a promising tool for wood breeders? Journal of Near Infrared Spectroscopy 22 (2): 141-146.
CrossRef | Gscholar

(16)

Hein PRG, Lima JT, Chaix GG (2010)
Otimização de calibrações baseadas em espectroscopia no infravermelho próximo para estimativa de propriedades da madeira de Eucalyptus [Optimization of calibrations based on near infrared spectroscopy to estimate Eucalyptus wood properties]. Floresta 40 (3): 615-624. [in Portuguese]
CrossRef | Gscholar

(17)

Hein PRG, Lima JT, Trugilho PF, Chaix G (2012)
Estimativa do ngulo microfibrilar em madeira de Eucalyptus urophylla × E. grandis por meio da espectroscopia no infravermelho próximo [Microfibrillar angle estimation in Eucalyptus urophylla × E. grandis wood using near infrared spectroscopy]. Floresta e ambiente 19 (2): 194-199. [in Portuguese]
CrossRef | Gscholar

(18)

Hodge GR, Acosta JJ, Unda F, Woodbridge WC, Mansfield SD (2018)
Global near infrared spectroscopy models to predict wood chemical properties of Eucalyptus. Journal of Near Infrared Spectroscopy 26: 117-132.
CrossRef | Gscholar

(19)

Hou S, Li L (2011)
Rapid characterization of woody biomass digestibility and chemical composition using near-infrared spectroscopy. Journal of Integrative Plant Biology 53 (2): 166-175.
CrossRef | Gscholar

(20)

Huang C, Han L, Yang Z, Liu X (2009)
Ultimate analysis and heating value prediction of straw by near infrared spectroscopy. Waste Management 29 (6): 1793-1797.
CrossRef | Gscholar

(21)

Li Y, Via BK, Li Y (2020)
Lifting wavelet transform for Vis-NIR spectral data optimization to predict wood density. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 240: 118566.
CrossRef | Gscholar

(22)

Lin SY, Dence CW (1992)
Methods in lignin chemistry. Springer-Verlag, Berlin, Germany, pp. 578.
Gscholar

(23)

Nunes CA, Freitas MP, Pinheiro ACM, Bastos SC (2012)
Chemoface: a novel free user-friendly interface for chemometrics. Journal of the Brazilian Chemical Society 23 (11): 2003-2010.
CrossRef | Gscholar

(24)

Obernberger I, Brunner T, Barnthaler G (2006)
Chemical properties of solid biofuels-significance and impact. Biomass and Bioenergy 30 (11): 973-982.
CrossRef | Gscholar

(25)

Panshin AJ, Zeeuw C (1970)
Textbook of wood technology. McGraw-Hill, New York, USA, pp. 705.
Gscholar

(26)

Pasquini C (2003)
Near infrared spectroscopy: fundamentals, practical aspects and analytical applications. Journal of the Brazilian Chemical Society 14 (2): 198-219.
CrossRef | Gscholar

(27)

Paula LER, Trugilho PF, Napoli A, Bianchi ML (2011)
Characterization of residues from plant biomass for use in energy generation. Cerne 17 (2): 237-246.
CrossRef | Gscholar

(28)

Pereira BLC, Carneiro ACO, Carvalho AMML, Colodette JL, Oliveira AC, Fontes MPF (2013)
Influence of chemical composition of Eucalyptus wood on gravimetric yield and charcoal properties. BioResources 8 (3): 4574-4592.
CrossRef | Gscholar

(29)

Pereira BLC, Oliveira AC, Carvalho AMML, Carneiro ACO, Santos LC, Vital BR (2012)
Quality of wood and charcoal from Eucalyptus clones for ironmaster use. International Journal of Forestry Research 2021: 1-8.
CrossRef | Gscholar

(30)

Prades C, Gomez-Sanchez I, Garcia-Olmo J, Gonzalez-Hernandez F, Gonzalez-Adrados JR (2014)
Application of VIS/NIR spectroscopy for estimating chemical, physical and mechanical properties of cork stoppers. Wood Science and Technology 48 (4): 811-830.
CrossRef | Gscholar

(31)

Protásio TP, Couto AM, Trugilho PF, Guimarães Junior JBGJ, Lima Junior PH, Silva MMO (2015)
Avaliação tecnológica do carvão vegetal da madeira de clones jovens de Eucalyptus grandis e Eucalyptus urophylla [Technological evaluation of wood charcoal from young clones of Eucalyptus grandis and Eucalyptus urophylla]. Scientia Forestalis 43 (108): 801-816. [in Portuguese]
CrossRef | Gscholar

(32)

Protásio TP, Trugilho PF, Araújo ACC, Bastos TA, Rosado SCS, Pinto JFN (2017)
Classificação de clones de Eucalyptus por meio da relação siringil/guaiacil e das características de crescimento para uso energético [Classification of Eucalyptus clones using the syringyl/guaiacyl ratio and growth characteristics for energy use]. Scientia Forestalis 45 (114): 327-341. [in Portuguese]
CrossRef | Gscholar

(33)

Ramadevi P, Hegde DV, Varghese M, Kamalakannan R, Ganapathy SP, Gurumurthy DS (2016)
Evaluation of lignin syringyl/guaiacyl ratio in Eucalyptus camaldulensis across three diverse sites based on near infrared spectroscopic calibration modelling with five Eucalyptus species and its impact on Kraft pulp yield. Journal of Near Infrared Spectroscopy 24 (6): 529-536.
CrossRef | Gscholar

(34)

Rosado LR, Takarada LM, Araújo ACC, Souza KRD, Hein PRG, Rosado SCS, Gonçalves FAM (2019)
Near infrared spectroscopy: rapid and accurate analytical tool for prediction of non-structural carbohydrates in wood. Cerne 25: 84-92.
CrossRef | Gscholar

(35)

Santos RC, Carneiro ACO, Vital BR, Castro RVO, Vidaurre GB, Trugilho PF, Castro AFM (2016)
Influência das propriedades químicas e da relação siringil/guaiacil da madeira de eucalipto na produção de carvão vegetal [Influence of chemical properties and syringyl/guaiacyl ratio of Eucalyptus wood on charcoal production]. Ciência Florestal 26 (2): 657 669. [in Portuguese]
CrossRef | Gscholar

(36)

Schimleck LR, Mora C, Daniels RF (2003)
Estimation of the physical wood properties of green Pinus taeda radial samples by near infrared spectroscopy. Canadian Journal of Forest Research 33: 2297-2305.
CrossRef | Gscholar

(37)

Schimleck LR, Stürzenbecher R, Jones PD, Evans R (2004)
Development of wood property calibrations using near infrared spectra having different spectral resolutions. Journal of Near Infrared Spectroscopy 12 (1): 55-61.
CrossRef | Gscholar

(38)

So CL, Eberhardt TL (2006)
Rapid analysis of inner and outer bark composition of Southern Yellow Pine bark from industrial sources. Holz Roh Werkst 64 (6): 463-467.
CrossRef | Gscholar

(39)

TAPPI (2000)
T280 pm-99 standard: Acetone extractives of wood and pulp. Technical Association of the Pulp and Paper Industry - TAPPI, Atlanta, USA.
Gscholar

(40)

Todorovic N, Popovic Z, Milic G (2015)
Estimation of quality of thermally modified beech wood with red heartwood by FT-NIR spectroscopy. Wood Science Technology 49: 527-549.
CrossRef | Gscholar

(41)

Tsuchikawa S, Kobori H (2015)
A review of recent application of near infrared spectroscopy to wood science and technology. Journal of Wood Science 61: 213-220.
CrossRef | Gscholar

(42)

Workman JJ, Weyer L (2008)
Practical guide to interpretive near-infrared spectroscopy. CRC press, Florida, USA, pp. 346.
Gscholar

(43)

Zanuncio AJV, Hein PRG, Carvalho AG, Rocha MFV, Carneiro ACO (2018)
Determination of heat-treated Eucalyptus and Pinus wood properties using NIR spectroscopy. Journal of Tropical Forest Science 30 (1): 117-125.
CrossRef | Gscholar

(44)

Zhou C, Jiang W, Via BK, Chetty PM, Swain T (2016)
Monitoring the chemistry and monosaccharide ratio of Eucalyptus dunnii wood by near infrared spectroscopy. Journal of Near Infrared Spectroscopy 24: 537-548.
CrossRef | Gscholar

(45)

Zhou C, Jiang W, Via BK, Fasinac O, Han G (2015)
Prediction of mixed hardwood lignin and carbohydrate content using ATR-FTIR and FT-NIR. Carbohydrate Polymers 121: 336-341.
CrossRef | Gscholar

iForest - Biogeosciences and Forestry

Contents

Search

Journal Info

Journal Subjects and Fields

For Authors

For Reviewers

For Readers

SISEF Publishing

Search iForest Contents