Abstract
Although visible and near-infrared (Vis-NIR) spectroscopy can rapidly and nondestructively identify wood species, the conventional spectrometer approach relies on the aggregate light absorption due to the chemical composition of wood and light scattering originating from the physical structure of wood. Hence, much of the work in this area is still limited to further spectral pretreatments, such as baseline correction and standard normal variate to reduce the light scattering effects. However, it should be emphasized that the light scattering rather than absorption in wood is dominant, and this must be effectively utilized to achieve highly accurate and robust wood classification. Here a novel method based on spatially resolved diffuse reflectance (wavelength range: 600–1000 nm) was demonstrated to classify 15 kinds of wood. A portable Vis-NIR spectral measurement system was designed according to previous simulations and experimental results. To simplify spectral data analysis (i.e., against overfitting), support vector machine (SVM) model was constructed for wood sample classification using principal component analysis (PCA) scores. The classification accuracies of 98.6% for five-fold cross-validation and 91.2% for test set validation were achieved. This study offers enhanced classification accuracy and robustness over other conventional nondestructive approaches for such various kinds of wood and sheds light on utilizing visible and short-wave NIR light scattering for wood classification.
Funding source: Japan Society for the Promotion of Science (JSPS) 10.13039/501100001691
Award Identifier / Grant number: 19K15886
Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: The authors are grateful for the financial support provided by JSPS (KAKENHI, no.19K15886).
Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
References
Abe, H., Watanabe, K., Ishikawa, A., Noshiro, S., Fujii, T., Iwasa, M., Kaneko, H., and Wada, H. (2016). Simple separation of Torreya nucifera and Chamaecyparis obtusa wood using portable visible and near-infrared spectrophotometry: differences in light-conducting properties. J. Wood Sci. 62: 210–212, https://doi.org/10.1007/s10086-016-1541-z.Search in Google Scholar
Baas, P., Blokhina, N., Fujii, T., Gasson, P.E., Grosser, D., Heinz, I., Ilic, J., Xiaomei, J., Miller, R., Newsom, L.A., et al.. (2004). IAWA list of microscopic features for softwood identification. IAWA J. 25: 1–70, https://doi.org/10.1163/2F22941932-90000496.Search in Google Scholar
Ban, M., Inagaki, T., Ma, T., and Tsuchikawa, S. (2018). Effect of cellular structure on the optical properties of wood. J. Near Infrared Spectrosc. 26: 53–60, https://doi.org/10.1177/0967033518757233.Search in Google Scholar
Braga, J.W.B., Pastore, T.C.M., Coradin, V.T.R., Camargos, J.A.A., and da Silva, A.R. (2011). The use of near infrared spectroscopy to identify solid wood specimens of swietenia macrophylla (cites appendix II). IAWA J. 32: 285–296, https://doi.org/10.1163/22941932-90000058.Search in Google Scholar
Boldrini, B., Kessler, W., Rebnera, K., and Kessler, R.W. (2012). Hyperspectral imaging: a review of best practice, performance and pitfalls for in-line and on-line applications. J. Near Infrared Spectrosc. 20: 483–508, https://doi.org/10.1255/jnirs.1003.Search in Google Scholar
D’Andrea, C., Farina, A., Comelli, D., Pifferi, A., Taroni, P., Valentini, G., and Cubeddu, R. (2007). Time-resolved diffuse optical spectroscopy of wood. Opt. InfoBase Conf. Pap. 62: 569–574, https://doi.org/10.1364/ECBO.2007.6633_59.Search in Google Scholar
Hwang, S.W., Horikawa, Y., Lee, W.H., and Sugiyama, J. (2016). Identification of Pinus species related to historic architecture in Korea using NIR chemometric approaches. J. Wood Sci. 62: 156–167, https://doi.org/10.1007/s10086-016-1540-0.Search in Google Scholar
Ishimaru, A. (1978). Wave propagation and scattering in random media. Academic Press, New York, 272. ISBN: 9780323158329.Search in Google Scholar
Kitamura, R., Inagaki, T., and Tsuchikawa, S. (2016). Determination of true optical absorption and scattering coefficient of wooden cell wall substance by time-of-flight near infrared spectroscopy. Optic Express 24: 3999–4009, https://doi.org/10.1364/oe.24.003999.Search in Google Scholar
Kobori, H., Inagaki, T., Fujimoto, T., Okura, T., and Tsuchikawa, S. (2015). Fast online NIR technique to predict MOE and moisture content of sawn lumber. Holzforschung 69: 329–335, https://doi.org/10.1515/hf-2014-0021.Search in Google Scholar
Lang, C., Costa, F.R.C., Camargo, J.L.C., Durgante, F.M., and Vicentini, A. (2015). Near infrared spectroscopy facilitates rapid identification of both young and mature Amazonian tree species. PloS One 10: 1–16, https://doi.org/10.1371/journal.pone.0134521.Search in Google Scholar
Lazarescu, C., Hart, F., Pirouz, Z., Panagiotidis, K., Mansfield, S.D., Barrett, J.D., and Avramidis, S. (2017). Wood species identification by near-infrared spectroscopy. Int. Wood Prod. J. 8: 32–35, https://doi.org/10.1080/20426445.2016.1242270.Search in Google Scholar
Ma, T., Inagaki, T., and Tsuchikawa, S. (2017). Calibration of silviscan data of Cryptomeria japonica wood concerning density and microfibril angles with NIR hyperspectral imaging with high spatial resolution. Holzforschung 71: 341–347, https://doi.org/10.1515/hf-2016-0153.Search in Google Scholar
Ma, T., Inagaki, T., Ban, M., and Tsuchikawa, S. (2018). Rapid identification of wood species by near-infrared spatially resolved spectroscopy (NIR-SRS) based on hyperspectral imaging (HSI). Holzforschung 73: 323–330, https://doi.org/10.1515/hf-2018-0128.Search in Google Scholar
Ma, T., Inagaki, T., and Tsuchikawa, S. (2019). Three-dimensional grain angle measurement of softwood (Hinoki cypress) using near infrared spatially and spectrally resolved imaging (NIR-SSRI). Holzforschung 73: 817–826, https://doi.org/10.1515/hf-2018-0273.Search in Google Scholar
Ma, T., Inagaki, T., and Tsuchikawa, S. (2020). Rapidly visualizing the dynamic state of free, weakly, and strongly hydrogen-bonded water with lignocellulosic material during drying by near-infrared hyperspectral imaging. Cellulose 27: 4857–4869, https://doi.org/10.1007/s10570-020-03117-6.Search in Google Scholar
Nisgoski, S., de Oliveira, A.A., and de Muñiz, G.I.B. (2017). Artificial neural network and SIMCA classification in some wood discrimination based on near-infrared spectra. Wood Sci. Technol. 51: 929–942, https://doi.org/10.1007/s00226-017-0915-8.Search in Google Scholar
Ohyama, M., Baba, K., and Itoh, T. (2001). Wood identification of Japanese Cyclobalanopsis species (Fagaceae) based on DNA polymorphism of the intergenic spacer between trnT and trnL 5′exon. J. Wood Sci. 47: 81–86, https://doi.org/10.1007/bf00780554.Search in Google Scholar
Pastore, T.C.M., Braga, J.W.B., Coradin, V.T.R., Magalhães, W.L.E., Okino, E.Y.A., Camargos, J.A.A., De Muñiz, G.I.B., Bressan, O.A., and Davrieux, F. (2011). Near infrared spectroscopy (NIRS) as a potential tool for monitoring trade of similar woods: discrimination of true mahogany, cedar, andiroba, and curupixá. Holzforschung 65: 73–80, https://doi.org/10.1515/hf.2011.010.Search in Google Scholar
Qin, J. and Lu, R. (2008). Measurement of the optical properties of fruits and vegetables using spatially resolved hyperspectral diffuse reflectance imaging technique. Postharvest Biol. Technol. 49: 355–365, https://doi.org/10.1016/j.postharvbio.2008.03.010.Search in Google Scholar
Tkachenko, N.V. (2006). Chapter 7 - Flash-photolysis. Opt. Spectrosc. 129–149, https://doi.org/10.1016/B978-044452126-2/50031-9.10.1016/B978-044452126-2/50031-9Search in Google Scholar
Tsuchikawa, S. and Kobori, H. (2015). A review of recent application of near infrared spectroscopy to wood science and technology. J. Wood Sci. 61: 213–220, https://doi.org/10.1007/s10086-015-1467-x.Search in Google Scholar
Tsuchikawa, S., Inoue, K., Noma, J., and Hayashi, K. (2003). Application of near-infrared spectroscopy to wood discrimination. J. Wood Sci. 49: 29–35, https://doi.org/10.1007/s10086-002-0471-0.Search in Google Scholar
Vapnik, V.N. (2010). The nature of statistical learning theory, 2nd ed. New York: Springer-Verlag, 314. ISBN:9781441931603.Search in Google Scholar
Wheeler, E.A., Baas, P., and Gasson, P.E. (1989). IAWA list of microscopic features for hardwood identification. IAWA Bull. 10: 219–332, https://doi.org/10.1163/22941932-90000496.Search in Google Scholar
Xing, Z., Wang, J., and Shen, G. (2008). Short-wave near-infrared spectroscopy for rapid. Quantification of acidity of aviation kerosene. Open Fuel Energy Sci. J. 1: 51–53, https://doi.org/10.2174/1876973x00801010051.Search in Google Scholar
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