IDENTIFICATION OF LIGNOCELLULOSE-LIKE MATERIAL USING SPECTROSCOPY ANALYSIS
DOI:
https://doi.org/10.59465/ijfr.2024.11.2.299-306Keywords:
Near Infrared Spectroscopy, Raman Spectroscopy, lignocellulose, lignocellulose-like materials, polyethyleneAbstract
Lignocellulose materials, such as bamboo, rattan, and wood, have been largely used for furniture and crafts. On the other hand, the utilization of lignocellulose-like materials, which have a similar texture and appearance to those from nature, has been increasing recently due to their superior durability. This research aimed to identify the lignocellulose-like material using spectroscopy analysis, such as Raman and Near Infrared (NIR) which is well-known as a non-destructive, quick, and accurate approach for material identification. We investigated 4 types of lignocellulose-like materials that were provided by Dewan Serat Indonesia (The Indonesian Fiber Council) from an industry that produces them. The NIR analysis was performed at wavenumbers 10,000-4,000 cm-1. The natural lignocellulose (bamboo and wood) and the polymers (polyethylene and polyproline) were used as standards. Raman analysis was further employed to identify the composition of selected lignocellulose-like materials by comparing their spectra with the library software. The results showed that the original NIR spectra of lignocellulose-like and those natural materials were different, indicating that the NIR analysis can differentiate those materials. The NIR spectra of lignocellulose-like materials were similar to those of polyethylene spectra. Those lignocellulose-like were also identified as polyethylene due to the similarity of the Raman spectra and their library spectra.
Downloads
References
Adi, D. S., Hwang, S., Pramasari, D. A., Amin, Y., Cipta, H., Damayanti, R., Dwianto, W., Sugiyama, J. (2020). Anatomical properties and near infrared spectra characteristics of four shorea species from Indonesia. HAYATI Journal of Biosciences, 27(3), 247–257. doi://10.4308/hjb.27.3.247.
Amigo, J. M., Babamoradi, H., & Elcoroaristizabal, S. (2015). Hyperspectral image analysis. A tutorial. Analytica Chimica Acta, 896, 34–51. doi://10.1016/j.aca.2015.09.030.
Amjad, A., Ullah, R., Khan, S., Bilal, M., & Khan, A. (2018). Raman spectroscopy based analysis of milk using random forest classification. Vibrational Spectroscopy, 99, 124–129. doi://10.1016/j.vibspec.2018.09.003.
Ayanleye, S., & Avramidis, S. (2021). Predictive capacity of some wood properties by near-infrared spectroscopy. International Wood Products Journal, 12(2), 83–94. doi://10.1080/20426445.2020.1834312.
Christine, M. G. F., Dachyar, M., & Nurcahyo, R. (2019). Product segmentation of wooden handicraft micro, small and medium enterprises (msmes) in indonesia. IOP Conference Series: Materials Science and Engineering, 598(1). doi://10.1088/1757-899X/598/1/012063.
chr, R., Prakasa, E., Krisdianto, Dewi, L. M., Wardoyo, R., Sugiarto, B., Pardede, H. F., Riyanto, Y., Astutiputri, V. F., Panjaitan, G. R., Hadiwidjaya, M. L., Maulana, Y. H., Mutaqin, I. N. (2019). Lignoindo: image database of indonesian commercial timber. IOP Conference Series: Earth and Environmental Science, 374(1). doi://10.1088/1755-1315/374/1/012057.
Deneva, V., Bakardzhiyski, I., Bambalov, K., Antonova, D., Tsobanova, D., Bambalov, V., Cozzzolino, D., Antonov, L. (2020). Using raman spectroscopy as a fast tool to classify and analyze bulgarian wines—a feasibility study. Molecules, 25(1), 1–10. doi://10.3390/molecules25010170.
Duan, Q., & Li, J. (2021). Classification of common household plastic wastes combining multiple methods based on near-infrared spectroscopy. ACS ES&T Engineering, 1(7), 1065–1073. doi://10.1021/acsestengg.0c00183.
Gierlinger, Notburga. (2018). New insights into plant cell walls by vibrational microspectroscopy. Applied Spectroscopy Reviews, 53(7), 517–551. doi://10.1080/05704928.2017.1363052.
Gopanna, A., Mandapati, R. N., Thomas, S. P., Rajan, K., & Chavali, M. (2019). Fourier transform infrared spectroscopy (ftir), Raman spectroscopy and wide-angle X-ray scattering (waxs) of polypropylene (pp)/cyclic olefin copolymer (coc) blends for qualitative and quantitative analysis. Polymer Bulletin, 76(8), 4259–4274. doi://10.1007/s00289-018-2599-0.
Herliana, S., Crestofel Lantu, D., Rosmiati, M., Chaerudin, R., & Lawiyah, N. (2022). Analysis of potential of indonesian craft exports. JASAE, 18(11), 1317–1325.
Himmi, S. K., Yoshimura, T., Yanase, Y., Torigoe, T., Akada, M., Ikeda, M., & Imazu, S. (2017). Volume visualization of hidden gallery system of drywood termite using computed tomography: a new approach on monitoring of termite infestation. In B. McLellan (Ed.), Sustainable Future for Human Security: Environment and Resources, VI, 61–68. doi://10.1007/978-981-10-5430-3_6.
Horikawa, Y., Mizuno-Tazuru, S., & Sugiyama, J. (2015). Near-infrared spectroscopy as a potential method for identification of anatomically similar Japanese diploxylons. Journal of Wood Science, 61, 251–261. doi://10.1007/s10086-015-1462-2.
Hwang, S. W., Horikawa, Y., Lee, W. H., & Sugiyama, J. (2016). Identification of Pinus species related to historic architecture in Korea using NIR chemometric approaches. Journal of Wood Science, 62(2), 156–167. doi://10.1007/s10086-016-1540-0.
Ismayati, M., Setiawan, K. H., Tarmadi, D., Zulfiana, D., Yusuf, S., & Santoso, B. (2011). The efficacy of organo-complex-based wood preservative formula against dry-wood termite cryptotermes cynocephalus light. Insects, 2(4), 491–498. doi://10.3390/insects2040491.
Iswanto, A. H., Madyaratri, E. W., Hutabarat, N. S., Zunaedi, E. R., Darwis, A., Hidayat, W., Susilowati, A., Adi, D. S., Lubis, M. A. R., Sucipto, T., Fatriasari, W., Antov, P., Savov, V., Hua, L. S. (2022). Chemical, physical , and mechanical properties of belangke bamboo (Gigantochloa pruriens) and its application as a reinforcing material in particleboard manufacturing. Polymers, 14(3111), 1–26. doi://10.3390/polym14153111.
Karimah, A., Ridho, M. R., Munawar, S. S., Adi, D. S., Ismadi, Damayanti, R., Subiyanto, B., Fatriasari, W., Fudholi, A. (2021). A review on natural fibers for development of eco-friendly bio-composite: characteristics, and utilizations. Journal of Materials Research and Technology. Elsevier Ltd. doi://10.1016/j.jmrt.2021.06.014.
Kida, T., Sasaki, T., Hiejima, Y., Maeda, S., & Nitta, K. hei. (2020). Rheo-Raman spectroscopic study of plasticity and elasticity transformation in poly(ether-block-amide) thermoplastic elastomers. Polymer, 189(November 2019), 122128. doi://10.1016/j.polymer.2019.122128.
Kniggendorf, A. K., Wetzel, C., & Roth, B. (2019). Microplastics detection in streaming tap water with raman spectroscopy. Sensors (Switzerland), 19(8), 12–14. doi://10.3390/s19081839.
Michel, A. P. M., Morrison, A. E., Preston, V. L., Marx, C. T., Colson, B. C., & White, H. K. (2020). Rapid identification of marine plastic debris via spectroscopic techniques and machine learning classifiers. Environmental Science and Technology, 54(17), 10630–10637. doi://10.1021/acs.est.0c02099.
Schwanninger, M., Rodrigues, J. C., & Fackler, K. (2011). A review of band assignments in near infrared spectra of wood and wood components. Journal of Near Infrared Spectroscopy. doi://10.1255/jnirs.955.
Shinzawa, H., Watanabe, R., Yamane, S., Koga, M., Hagihara, H., & Mizukado, J. (2021). Aging of polypropylene probed by near infrared spectroscopy. Journal of Near Infrared Spectroscopy, 29(5), 259–268. doi://10.1177/0967033521999115.
Tsuchikawa, S., & Kobori, H. (2015). A review of recent application of near infrared spectroscopy to wood science and technology. Journal of Wood Science, 61(3), 213–220. doi://10.1007/s10086-015-1467-x.
Tuncer, F. D., Dogu, D., & Akdeniz, E. (2021). Efficiency of preprocessing methods for discrimination of anatomically similar pine species by NIR spectroscopy. Wood Material Science & Engineering, 0(0), 212–221. doi://10.1080/17480272.2021.2012821.
Wang, Y., Xiang, J., Tang, Y., Chen, W., & Xu, Y. (2022). A review of the application of near-infrared spectroscopy (NIRS) in forestry. Applied Spectroscopy Reviews, 57(4), 300–317. doi://10.1080/05704928.2021.1875481.
Widjaja, E. A., Rahayunigsih, Y., Rahajoie, J. S., Ubaidillah, R., Maryanto, I., Walujo, E. B., & Semiadi, G. (2014). Kekinian Keanekaragaman Hayati Indonesia 2014. Jakarta: LIPI Press.
Xia, J., Huang, Y., Li, Q., Xiong, Y., & Min, S. (2021). Convolutional neural network with near-infrared spectroscopy for plastic discrimination. Environmental Chemistry Letters, 19(5), 3547–3555. doi://10.1007/s10311-021-01240-9.
Yamauchi, S., Iijima, Y., & Doi, S. (2005). Spectrochemical characterization by ft-raman spectroscopy of wood heat-treated at low temperatures: Japanese larch and beech. Journal of Wood Science, 51(5), 498–506. doi://10.1007/s10086-004-0691-6.
Zhou, X. X., Liu, R., Hao, L. T., & Liu, J. F. (2021). Identification of polystyrene nanoplastics using surface enhanced raman spectroscopy. Talanta, 221(August 2020), 121552. doi://10.1016/j.talanta.2020.121552.

Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Indonesian Journal of Forestry Research

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
All articles published in Indonesian Journal of Forestry Research (IJFR) are licensed under the terms of the Creative Commons Attribution International License (CC BY-NC-SA 4.0) with CC BY-NC-SA 4.0 being the latest version.