Abstract

This study investigated the feasibility of using near infrared (NIR) spectroscopy and multivariate calibration to predict bulk density and stiffness of 3.2 mm thick yellow poplar veneer strips. Full-range (800–2500 nm) raw NIR spectra and spectra pre-treated using the first derivative method, along with spectra from three other different wavelength windows of 1200–2400 nm, 1800–2400 nm and 1400–2000 nm were regressed against the bulk density (kg m⁻³) values and the dynamic modulus of elasticity (stiffness; GPa) of the veneers using the projection to latent structures (PLS) method to develop calibration models. All predictive models developed performed well in the prediction of bulk density and stiffness of new test samples that were not included in the calibration models. R² values ranged from 0.67-0.78 and 0.56-0.72, respectively, for bulk density and stiffness. There was significant improvement in models developed with first derivative spectra over models developed with raw spectra. The models developed using the first derivative used fewer latent variables to achieve predictive models with higher R² values, lower root mean square errors of prediction (RMSEP) and standard errors of prediction (SEP). Models developed using the full NIR spectral range (800–2500 nm) and the NIR spectral region of 1200–2400 nm performed better than models developed using the restricted NIR wavelength regions of 1800–2400 nm and 1400–2000 nm. However, there was no clear distinction between models developed using the full NIR spectral range and the NIR spectral region of 1200–2400 nm. Overall, models developed with the first derivative pre-processed spectra using the whole NIR spectrum performed best in predictability. The results of this study show the potential of using multivariate data analysis coupled with NIR spectroscopy for on-line sorting and assessment of veneer stiffness prior to the lay-up process in the manufacturing of veneer-based engineered wood products such as plywood, Parallam and laminated veneer lumber.

© 2008 IM Publications LLP

Extraction of information from laser-induced breakdown spectroscopy spectral data by multivariate analysis

Nicole Labbé, Isabel Maya Swamidoss, Nicolas André, Madhavi Z. Martin, Timothy M. Young, and Timothy G. Rials
Appl. Opt. 47(31) G158-G165 (2008)

Combining light polarization and speckle measurements with multivariate analysis to predict bulk optical properties of turbid media

Daphné Héran, Maxime Ryckewaert, Yannick Abautret, Myriam Zerrad, Claude Amra, and Ryad Bendoula
Appl. Opt. 58(30) 8247-8256 (2019)

Evaluation of univariate and multivariate calibration strategies for the direct determination of total carbon in soils by laser-induced breakdown spectroscopy: tutorial

Wesley Nascimento Guedes, Diego Victor Babos, Vinícius Câmara Costa, Carla Pereira De Morais, Vitor da Silveira Freitas, Kleydson Stenio, Alfredo Augusto Pereira Xavier, Luís Carlos Leva Borduchi, Paulino Ribeiro Villas-Boas, and Débora Marcondes Bastos Pereira Milori
J. Opt. Soc. Am. B 40(5) 1319-1330 (2023)

Spatial mapping of proteoglycan content in articular cartilage using near-infrared (NIR) spectroscopy

Isaac O. Afara, Hayley Moody, Sanjleena Singh, Indira Prasadam, and Adekunle Oloyede
Biomed. Opt. Express 6(1) 144-154 (2015)

Rapid detection of carbon-nitrogen ratio for anaerobic fermentation feedstocks using near-infrared spectroscopy combined with BiPLS and GSA

Jinming Liu, Nan Li, Feng Zhen, Yonghua Xu, Wenzhe Li, and Yong Sun
Appl. Opt. 58(18) 5090-5097 (2019)