Estimation of physical and mechanical properties of agro-based particleboards by near infrared spectroscopy

Partial least square regression (PLS-R) calibrations based on near infrared (NIR) spectroscopic data were developed in order to predict mechanical and physical properties of agro-based particleboards. The panels were manufactured using Eucalyptus and Pinus wood particles and sugar cane bagasse. The...

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Bibliographic Details
Published inEuropean journal of wood and wood products Vol. 69; no. 3; pp. 431 - 442
Main Authors Hein, Paulo Ricardo Gherardi, Campos, Ana Carolina Maioli, Mendes, Rafael Farinassi, Mendes, Lourival Marin, Chaix, Gilles
Format Journal Article
LanguageEnglish
German
Published Berlin/Heidelberg Springer-Verlag 01.08.2011
Springer Nature B.V
Springer Verlag
Subjects
Bagasse
Biomedical and Life Sciences
Bonding strength
Cell adhesion
Cellulose
Cellulosic resins
Ceramics
Composites
Eucalyptus
Glass
Hardwoods
Infrared spectroscopy
Life Sciences
Lignin
Machines
Manufacturing
Mechanical properties
Modulus of elasticity
Modulus of rupture
Natural Materials
Near infrared radiation
Originals Originalarbeiten
Particle board
Physical properties
Processes
Resins
Spectrum analysis
Submerging
Sugarcane
Water absorption
Wood Science & Technology
Sugar Cane Bagasse
Partial Little Square Regression
Partial Little Square Model
Partial Little Square
Internal Bond
Physical Sciences
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Summary:Partial least square regression (PLS-R) calibrations based on near infrared (NIR) spectroscopic data were developed in order to predict mechanical and physical properties of agro-based particleboards. The panels were manufactured using Eucalyptus and Pinus wood particles and sugar cane bagasse. The following panel properties were evaluated according to standard methods: modulus of elasticity (MOE), modulus of rupture (MOR), internal bonding (IB) strength, water absorption (WA24H), and thickness swelling (TS24H) after 24 hours of immersion. NIR spectra information was measured on samples cut from each particleboard and correlated with their physical and mechanical properties by PLS-R to build predictive NIR models. The NIR models for IB, WA24H and TS24H presented satisfactory coefficient of determination (0.73; 0.72 and 0.75, respectively.) The key role of resins (adhesives), cellulose, and lignin for NIRS calibrations of mechanical and physical properties of the particleboards is shown. These models can be useful to quickly verify such properties in unknown agro-based particleboards.
ISSN:0018-3768
1436-736X
DOI:10.1007/s00107-010-0471-5