Identifying Timber Species by Diffuse Reflection Spectra in the Near-Infrared Region Using a Linear Discriminant Analysis
https://doi.org/10.30764/1819-2785-2022-1-50-57
Abstract
The article presents an algorithm of analysis of coniferous timber materials (Pinus sylvestris L., Pinus sibirica Du Tour, Larix decidua Mill., Abies alba L. and Picea abies (L.) Karst.) using the spectroscopy method in the near-infrared region. Timber is a complex organic material consisting of cellulose, hemicellulose, lignin, and extractive substances. These compounds generate absorption bands in the near-infrared region which are mainly overtones and combination bands of the O-H, N-H and C-H functional groups. The authors suggest the best ways to correct scattering considering the specific features of the samples under examination. Based on the application of linear discriminant analysis, a method is proposed for the automatic identification of the timber species by diffuse reflection spectra in the near-infrared region, as well as key parameters sufficient for the classification procedure are determined. The obtained LDA-models showed high predictive capability. The overall average classification accuracy reached 97,6%. Based on the results obtained, it can be stated that the NIR spectroscopy is suitable for solving tasks related to the identification of the timber species; it has proven to be an effective tool for distinguishing wood species.
About the Authors
A. N. Khokh
State Institution “Scientific and Practical Center of the State Forensic Examination Committee of the Republic of Belarus”
Belarus
Belarus
Khokh Anna Nikolaevna – Head of Laboratory of Materials, Substances and Products Research
Minsk 220114
V. B. Zvyagintsev
Educational Institution “Belarusian State Technological University”
Belarus
Belarus
Zvyagintsev Vyacheslav Borisovich – Candidate of Biology, Assistant Professor, Head of Department of Forest Protection and Wood Science
Minsk 220006
References
1. Toscano P., Iannotta N., Scalercio S. Botanical and Agricultural Aspects: Agronomic Techniques and Orchard Management. In: Muzzalupo I., Micali S. (Eds.). Agricultural and Food Biotechnologies of Olea europaea and Stone Fruits. Bentham, 2015. P. 3–73. https://doi.org/10.2174/9781608059935115010004
2. Ohyama M., Baba K., Itoh T. Wood Identification of Japanese Cyclobalanopsis Species (Fagaceae) Based on DNA Polymorphism of the Intergenic Spacer between trnT and trnL 5′ exon. Journal of Wood Science. 2001. Vol. 47. No. 2. P. 81–86. https://doi.org/10.1007/BF00780554
3. Sharma V., Yadav J., Kumar R., Tesarova D., Ekielski A., Mishra P. On the Rapid and Non-Destructive Approach for Wood Identification Using ATR-FTIR Spectroscopy and Chemometric Methods. Vibrational Spectroscopy. 2020. Vol. 110. 103097. https://doi.org/10.1016/j.vibspec.2020.103097
4. Kanayama H., Ma T., Tsuchikawa S., Inagaki T. Cognitive Spectroscopy for Wood Species Identification: Near Infrared Hyperspectral Imaging Combined with Convolutional Neural Networks. Analyst. 2019. Vol. 144. No. 21. P. 6438–6446. https://doi.org/10.1039/C9AN01180C
5. Adedipe O.E., Dawson-Andoh B., Slahor J., Osborn L. Classification of Red Oak (Quercus rubra) and White Oak (Quercus alba) Wood Using a Near Infrared Spectrometer and Soft Independent Modelling of Class Analogies. Journal of Near Infrared Spectroscopy. 2008. Vol. 16. No. 1. P. 49–57. https://doi.org/10.1255/jnirs.760
6. Manley M., Baeten V. Spectroscopic Technique: Near Infrared (NIR) Spectroscopy. In: Modern Techniques for Food Authentication. 2nd ed. Academic Press, 2018. P. 51–102. https://doi.org/10.1016/B978-0-12-8142646.00003-7
7. Aenugu H.P.R., Kumar D.S., Srisudharson N.P., Ghosh S., Banji D. Near Infra Red Spectroscopy – An Overview. International Journal of ChemTech Research. 2011. Vol. 3. No. 2. P. 825–836.
8. Khokh A.N., Zviagintsev V.B. Implementation of Near-Infrared Spectroscopy to Identify Coniferous Wood Breeds. Issues of Criminology, Forensics and Forensic Examination. 2020. No. 1. P. 161–167. (In Russ.).
9. Paul L.C., Suman A.A., Sultan N. Methodological Analysis of Principal Component Analysis (PCA) Method. International Journal of Computational Engineering & Management. 2013. Vol. 16. No. 2. P. 32–38.
10. Boedeker P., Kearns N.T. Linear Discriminant Analysis for Prediction of Group Membership: A User-Friendly Primer. Advances in Methods and Practices in Psychological Science. 2019. Vol. 2. No. 3. P. 250–263. https://doi.org/10.1177/2515245919849378.
11. Tharwat A., Gaber T., Ibrahim A., Hassanien A.E. Linear Discriminant Analysis: A Detailed Tutorial. AI Communications. 2017. Vol. 30. No. 2. P. 169–190. https://doi.org/10.3233/AIC-170729
Review
For citations:
Khokh A.N., Zvyagintsev V.B. Identifying Timber Species by Diffuse Reflection Spectra in the Near-Infrared Region Using a Linear Discriminant Analysis. Theory and Practice of Forensic Science. 2022;17(1):50-57. (In Russ.) https://doi.org/10.30764/1819-2785-2022-1-50-57
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