Hydromechanical behavior of wood during drying studied by NIR spectroscopy and image analysis

Evelize Aparecida Amaral; Lívia Freire Baliza; Luana Maria dos Santos; André Tetsuo Shashiki; Paulo Fernando Trugilho; Paulo Ricardo Gherardi Hein

doi:10.1515/hf-2023-0030

Article

Hydromechanical behavior of wood during drying studied by NIR spectroscopy and image analysis

Evelize Aparecida Amaral , Lívia Freire Baliza , Luana Maria dos Santos , André Tetsuo Shashiki , Paulo Fernando Trugilho and Paulo Ricardo Gherardi Hein

Published/Copyright: June 30, 2023

Published by

Become an author with De Gruyter Brill

Submit Manuscript Author Information

From the journal Holzforschung Volume 77 Issue 8

Abstract

The physical properties of wood are important parameters to qualify the material. However, as it is a heterogeneous material, moisture content and wood contractions may vary within the sample. Thus, the objective was to monitor the hydromechanical behavior of wood during drying using near infrared (NIR) spectroscopy and image analysis. Equidistant points were marked on the radial surface of a wooden board and NIR spectra were recorded at each marking during drying of the piece. After spectral acquisition in each drying step, images were obtained and the markings were referenced to monitor contractions during drying. Moisture content (MC) estimates via NIR spectra showed strong correlation with reference values (R²cv = 0.92, RMSEcv = 9.82 %). From the estimates it was possible to generate graphic images to visualize and quantify the spatial variation of MC and shrinkage during wood drying. In the initial stages of drying, the ends of the material showed high moisture in relation to the center of the sample. However, MC loss was 11 % greater at the ends of the wood board when compared to its interior while the shrinkage in external zones was 3 times greater than the internal part. The use of NIR technique associated with image analysis can be a promising tool for estimating moisture contents and contractions in wood.

Keywords: desorption monitoring; shrinkage; wood quality; wood–water relationships

Corresponding author: Evelize Aparecida Amaral, Departamento de Ciências Florestais, DCF, Universidade Federal de Lavras, 37200-900 Lavras, MG, Brazil, E-mail: evelizeamaral@yahoo.com.br

Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: This project was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001, by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq: grants n. 406593/2021-3) and by Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG). P. R. G. Hein was supported by CNPq grants (process no. 309620/2020-1). This study was also supported by the Wood Science and Technology Graduation Program (DCF/UFLA, Brazil).
Conflict of interest statement: The authors declare that they have no conflicts of interest regarding this article.

References

Amaral, E.A., Santos, L.M., Costa, E.V.S., Trugilho, P.F., and Hein, P.R.G. (2020). Estimation of moisture in wood chips by near infrared spectroscopy. Maderas. Cienc. Tecnol. 22: 291–302, https://doi.org/10.4067/s0718-221x2020005000304.Search in Google Scholar

Associação Brasileira de Normas Técnicas (ABNT) (2003). NBR 14929: Madeira – determinação do teor de umidade de cavacos – método por secagem em estufa, Rio de Janeiro.Search in Google Scholar

Baliza, L.F., Silva, C.H., Amaral, E.A., Ramalho, F.M.G., Trugilho, P.F., Sun, H., and Hein, P.R.G. (2022). Evaluation of the spatial variation in moisture content inside wood pieces during drying by NIR spectroscopy. Holzforschung 77: 95–105, https://doi.org/10.1515/hf-2022-0123.Search in Google Scholar

Costa, E.V.S., Rocha, M.F.V., Hein, P.R.G., Amaral, E.A., Santos, L.M., Brandão, L.E.V.S., and Trugilho, P.F. (2018). Influence of spectral acquisition technique and wood anisotropy on the statistics of predictive near infrared–based models for wood density. J. Near Infrared Spectrosc. 26: 106–116, https://doi.org/10.1177/0967033518757070.Search in Google Scholar

Diniz, C.P., Grattapaglia, D., Mansfield, S.D., and Figueiredo, L.F.A. (2019). Near infrared-based models for lignin syringyl/guaiacyl ratio of Eucalyptus benthamii and E. pellita using a streamlined thioacidolysis procedure as the reference method. Wood Sci. Technol. 53: 521–533, https://doi.org/10.1007/s00226-019-01090-3.Search in Google Scholar

Fujimoto, T., Kurata, Y., Matsumoto, K., and Tsuchikawa, S. (2008). Application of near infrared spectroscopy for estimating wood mechanical properties of small clear and full length lumber specimens. J. Near Infrared Spectrosc. 16: 529–537, https://doi.org/10.1255/jnirs.818.Search in Google Scholar

Haddadi, A., Hans, G., Leblon, B., Pirouz, Z., Tsuchikawa, S., Nader, J., and Groves, K. (2016). Determination of optical parameters and moisture content of wood with visible-near infrared spectroscopy. J. Near Infrared Spectrosc. 6: 571–585, https://doi.org/10.1255/jnirs.1174.Search in Google Scholar

Hans, G., Leblon, B., Stirling, R., Nader, J., and LaRocque, A. (2013). Monitoring of moisture content and basic specific gravity in black spruce logs using a hand-held MEMS-based near-infrared spectrometer. For. Chron. 89: 605–618, https://doi.org/10.5558/tfc2013-112.Search in Google Scholar

Hein, P.R.G., Bouvet, J.-M., Mandrou, E., Vigneron, P., Clair, B., and Chaix, G. (2013). Age trends of microfibril angle inheritance and their genetic and environmental correlations with growth density and chemical properties in Eucalyptus urophylla ST Blake wood. Ann. For. Sci. 6: 681–691, https://doi.org/10.1007/s13595-012-0186-3.Search in Google Scholar

Kobori, H., Gorretta, N., Rabatel, G., Bellon Maurel, V., Chaix, G., Roger, J.-M., and Tsuchikawa, S. (2013). Applicability of Vis-NIR hyperspectral imaging for monitoring wood moisture content (MC). Holzforschung 67: 307–314, https://doi.org/10.1515/hf-2012-0054.Search in Google Scholar

Martens, H. and Tormod, N. (1989). Multivariate calibration. J. Wiley, New York, pp. 428.Search in Google Scholar

Naes, T.I., Fearn, T., and Davies, T. (2002). User-friendly guide to multivariate calibration and classification. J. Chemom. 10: 571–572.Search in Google Scholar

Nascimento, T.M., Monteiro, T.C., Baraúna Moulin, E.E.P.J.C., and Azevedo, A.M. (2019). Drying influence on the development of cracks in Eucalyptus logs. Bioresources 1: 220–233, https://doi.org/10.15376/biores.14.1.220-233.Search in Google Scholar

Nunes, C.A., Freitas, M.P., Pinheiro, A.C.M., and Bastos, S.C. (2012). Chemoface: a novel free user-friendly interface for chemometrics. J. Braz. Chem. Soc. 23: 2003–2010, https://doi.org/10.1590/s0103-50532012005000073.Search in Google Scholar

Pasquini, C. (2018). Near infrared spectroscopy: a mature analytical technique winew perspective – a review. Anal. Chim. Acta 5: 8–36, https://doi.org/10.1016/j.aca.2018.04.004.Search in Google Scholar PubMed

Pecoraro, E., Pizzo, B., Alves, A., Macchioni, N., and Rodrigues, J. (2015). Measuring the chemical composition of waterlogged decayed wood by near infrared spectroscopy. Microchem. J. 122: 176–188, https://doi.org/10.1016/j.microc.2015.05.005.Search in Google Scholar

Perré, P. (1997). Image analysis homogenization, numerical simulation and experiment as complementary tools to enlighten the relationship between wood anatomy and drying behavior. Dry. Technol. 15: 2211–2238, https://doi.org/10.1080/07373939708917359.Search in Google Scholar

Santos, L.M., Amaral, E.A., Nieri, E.M., Costa, E.V.S., Trugilho, P.F., Calegário, N., and Hein, P.R.G. (2020). Estimating wood moisture by near infrared spectroscopy: testing acquisition methods and wood surfaces qualities. Wood Mater. Sci. Eng. 16: 336–343, https://doi.org/10.1080/17480272.2020.1768143.Search in Google Scholar

Schwanninger, M., Rodrigues, J., and Fackler, K. (2011). A review of band assignments in near infrared spectra of wood and wood components. J. Near Infrared Spectrosc. 19: 287–308, https://doi.org/10.1255/jnirs.955.Search in Google Scholar

Sofianto, I.A., Inagaki, T., Itoh, M., and Tsuchikawa, S. (2017). Stiffness and moisture content prediction model of wooden veneer using fast online near-infrared (NIR) spectroscopic system. For. Res. Eng.: Int. J. 2: 1–5, https://doi.org/10.15406/freij.2017.01.00006.Search in Google Scholar

Wang, Y., Xiang, J., Tang, Y., Chen, W., and Xu, Y. (2021). A review of the application of near-infrared spectroscopy (NIRS) in forestry. Appl. Spectrosc. Rev. 4: 1–18, https://doi.org/10.1080/05704928.2021.1875481.Search in Google Scholar

Watanabe, K., Lazarescu, C., Shida, S., and Avramidis, S. (2012). A novel method of measuring moisture content distribution in timber during drying using CT scanning and image processing techniques. Dry. Technol. 30: 256–262, https://doi.org/10.1080/07373937.2011.634977.Search in Google Scholar

Williams, P.C. and Sobering, D.C. (1993). Comparison of commercial near infrared transmittance and reflectance instruments for analysis of whole grains and seeds. J. Near Infrared Spectrosc. 1: 25–33, https://doi.org/10.1255/jnirs.3.Search in Google Scholar

Zen, L.R. (2016). Combined methods for drying Eucalyptus wood: pre-drying in the open air with drying in a conventional oven, Master’s thesis. Curitiba, Federal University of Paraná p. 105.Search in Google Scholar

Zhang, H., Li, Y., Zhang, H., Zhang, Y., and Li, P. (2011). Application of near infrared spectroscopy in wood water content prediction. Adv. Mater. Res. 1: 434–438, https://doi.org/10.4028/www.scientific.net/amr.187.434.Search in Google Scholar

Received: 2023-03-17

Accepted: 2023-06-07

Published Online: 2023-06-30

Published in Print: 2023-08-28

You are currently not able to access this content.

Articles in the same Issue

https://doi.org/10.1515/hf-2023-0030

Keywords for this article

desorption monitoring; shrinkage; wood quality; wood–water relationships