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2023 Vol.55, Issue 5 Preview Page

Research Article

Predictive Modeling for Degree of Substitution of Cellulose Acetate using Infrared Spectroscopy and Machine Learning

적외선 분광법과 기계학습을 이용한 셀룰로오스 아세테이트의 치환도 예측 모델링

Yong Ju Lee1
,
Ji Eun Lee2
,
Jae Gyoung Gwon3
,
Tai Ju Lee3†
,
Hyoung Jin Kim4‡
이 용주1
,
이 지은2
,
권 재경3
,
이 태주3†
,
김 형진4‡
1Department of Forest Products and Biotechnology, Kookmin University, Graduate Student
2Division of Forest Industrial Materials, Department of Forest Products and Industry, National Institute of Forest Science
3Division of Forest Industrial Materials, Department of Forest Products and Industry, National Institute of Forest Science
4Department of Forest Products and Biotechnology, Kookmin University, Professor
1Department of Forest Products and Biotechnology, Kookmin University, Graduate Student
2국립산림과학원 임산자원이용연구부 임산소재연구과, 연구원
3국립산림과학원 임산자원이용연구부 임산소재연구과, 연구사
4국민대학교 과학기술대학 임산생명공학과, 교수
†Corresponding author: E-Mail: leetj@korea.kr (Address: Department of Forest Products and Industry, Division of Forest Industrial Materials, National Institute of Forest Science, Seoul, 02455, Republic of Korea)
‡Co-corresponding Author: E-Mail: hyjikim@kookmin.ac.kr (Address: Department of Forest Products & Biotechnology, College of Science and Technology, Kookmin University, Seoul, Republic of Korea)
30 October 2023. pp. 83-95
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Abstract

The objective of this study is to apply FTIR and machine learning models for the quantitative analysis of the degree of substitution of cellulose acetate. The models used for the degree of substitution analysis include PCA (principal component analysis), PLS-DA (partial least squares discriminant analysis), SVM (support vector machine), and KNN (k-nearest neighbor). The critical findings of this study indicated that it is possible to analyze the degree of substitution for cellulose acetate with a degree of substitution of 2.0 or less using IR spectrum data derived from acetylation, estimated through PCA. The decrease in explanatory power for degrees of substitution higher than 2.0 can be attributed to the chemical reaction rate. However, by applying SVM and utilizing the kernel trick to project the data into a high-dimensional feature space and perform non-linear classification, it was possible to create a degree of substitution discrimination model with excellent performance, regardless of the degree of substitution. As a result, the model for analyzing the degree of substitution of polymer monomers based on machine learning and IR spectrum data was proposed. It is believed that this model can efficiently replace existing analytical methods.

Keywords
Cellulose acetate
degree of substitution
machine learning
infrared spectroscopy
partial least squares-discriminant analysis (PLS-DA)
support vector machine (SVM)
K-nearest neighbor (KNN)
References
1
Filho, G. R., Monteiro, D. S., Meireles, C. S., Assuncao, R. M. N., Cerqueira, D. A., Barud, H. S., Ribeiro, S. L., and Messadeq, Y., Synthesis and characterization of cellulose acetate produced from recycled newspaper, Carbohydrate Polymers, 73(1): 74-82 (2008). 10.1016/j.carbpol.2007.11.010
2
Biswas, A., Saha, B. C., Lawton, J. W., Shogren, R. L., and Willett, J. L., Process for obtaining cellulose acetate from agricultural by-products, Carbohydrate Polymers, 64(1): 134-137 (2006). 10.1016/j.carbpol.2005.11.002
3
Cai, J., Zhou, R., Li, T., He, J., Wang, G., Wang, H., and Xiong, H., Bamboo cellulose-derived cellulose acetate for electrospun nanofibers: synthesis m characterization and kinetics, Cellulose, 25: 391-398 (2018). 10.1007/s10570-017-1604-3
4
Peredo, K., Reyes, H., Escobar, D., Vega-Lara, J., Berg, A., and Pereira, M., Acetylation of bleached kraft pulp: Effect of xylan content on properties of acetylation compounds, Carbohydrate Polymers, 117: 1014-1020 (2015). 10.1016/j.carbpol.2014.10.00425498729
5
Popescu, C., Larsson, P. T., Olaru, N., and Vasile, C., Spectroscopic study of acetylated kraft pulp fibers, Carbohydrate Polymers, 88: 530-536 (2012). 10.1016/j.carbpol.2011.12.046
6
de Freitas, R. R. M., Senna, A. M., and Botaro, V. R., Influence of degree of substitution on thermal dynamic mechanical and physicochemical properties of cellulose acetate, Industrial Crops & Products, 109, 452-458 (2017). 10.1016/j.indcrop.2017.08.062
7
Wolf, J., Scheelje, F. C. M., Matveyeva, O., and Meier, M A. R., Determination of the degree of substitution of cellulose ester via ATR-FTIR spectroscopy, Journal of Polymer Science, 1-11 (2023).
8
Ass, B. A. P., Frollini, E., and Heinze, T., Studies on the homogeneous acetylation of cellulose in the novel solvent dimethyl sulfoxide/tetrabutylammonium fluoride trihydrate, Macromolecular Bioscience, 4(11): 1008-1013 (2004). 10.1002/mabi.20040008815529395
9
Abbott, A. P., Bell, T. J., Handa, S., and Stoddart, B., O-acetylation of cellulose and monosaccharides using a zinc-based ionic liquid, Green Chemistry, 7(10): 705-707 (2005). 10.1039/b511691k
10
J. Wu, Z Han G, J., Ge, J., Ren, Q., and Guo, M., Homogeneous acetylation of cellulose in a new ionic liquid, Biomacromolecules, 5(2): 266-268 (2004). 10.1021/bm034398d15002983
11
Goldstein, I. S., Jeroski, E. B., Lund, A. E., Nielson, J. F., and Weaver, J. W., Acetylation of wood in lumber thickness, Forest Products Journal 11:363-370 (1961).
12
Samios, E., Dart, R. K., and Dawkins, J. V., Preparation, characterization and biodegradation studies on cellulose acetates with varying degrees of substitution, Polymer 38:3045-3054 (1997). 10.1016/S0032-3861(96)00868-3
13
Fei, P., Liao, L., Cheng, B., and Song, J., Quantitative analysis of cellulose acetate with a high degree of substitution by FTIR and its application, Analytical Methods 9:6194-6201 (2017). 10.1039/C7AY02165H
14
Lee, Y. J., Kim, J. H., and Kim, K. J., Application of infrared spectroscopy to analyze the degree of substitution in acetylated wood powder, Journal of KTAPPI 54(4):19-24 (2022). 10.7584/JKTAPPI.2022.08.54.4.19
15
Lee, Y. J., Cha, J. E., Kang, N. Y., Kim, J. H., and Kim, H. J., Application of spectroscopy to analyze the degree of substitution of carboxymethyl cellulose, Journal of Korea TAPPI 55(1):103-111 (2023). 10.7584/JKTAPPI.2023.2.55.1.103
16
Costa, L. R., Tonoli, G. H. D., Milagres, F. R., and Hein, P. R. G., Artificial neural network and partial least square regressions for rapid estimation of cellulose pulp dryness based on near-infrared spectroscopic data, Carbohydrate polymers 224:115186 (2019). 10.1016/j.carbpol.2019.11518631472836
17
Hwang, S. W., Chung, H., Lee, T., Kim, J., Kim, Y., Kim, J. C., Kwak, H. W., Choi, I. G., and Yeo, H., Feature importance measures from random forest regressor using near-infrared spectra for predicting carbonization characteristics of kraft lignin-derived hydrochar, Journal of Wood Science 69(1):1-12 (2023). 10.1186/s10086-022-02073-y
18
Lee, T. J., Oh, Y. J., Kwon, J. K., Hwang, K. J., Park, J. S., and Seo, J. H., Influence of wood pulp properties on the efficiency of carboxymethylation, Journal of KTAPPI 54(4):85-93 (2022). 10.7584/JKTAPPI.2022.08.54.4.85
19
Savitzky, A. and Golay, M. J. E., Smoothing and differentiation of data by simplified least squares procedures, Analytical Chemistry 36(8):1627-1639 (1964). 10.1021/ac60214a047
20
Hwang, S. W., Horikawa, Y., Lee, W. H., and Sugiyama, J., Identification of Pinus species related to historic architecture in Korea using NIR chemometric approaches, Journal of Wood Science 62(2):156-167 (2016). 10.1007/s10086-016-1540-0
21
Lee, S. D., Lohumi, S., Cho, B. K., Kim, M. S., and Lee, S. H., Development of nondestructive detection method for adulterated powder products using Raman spectroscopy and partial least squares regression, Journal of the Korean Society for Nondestructive Testing 34(4):283-289 (2014). 10.7779/JKSNT.2014.34.4.283
22
Samanta, B. I. S. W. A. J. I. T., Al-Balushi, K. R., and Al-Araimi, S. A., Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection, Engineering Applications of Artificial Intelligence 16(7-8):657-665 (2003). 10.1016/j.engappai.2003.09.006
23
Vert, J. P., Tsuda, K., and Schölkopf, B., A primer on kernel methods, Kernel Methods in Computational Biology 47:35-70 (2004).
24
Tsuchikawa, S., Yamato, K., and Inoue, K. Discriminant analysis of wood-based materials using near-infrared spectroscopy, Journal of Wood Science 49:275-280 (2003). 10.1007/s10086-002-0471-0
25
Abraham, S., Huynh, C., and Vu, H., Classification of soils into hydrologic groups using machine learning, Data 5(1):2 (2019). 10.3390/data5010002
26
Heo, T. I., Kim, D. H., and Hwang, S. W., Identification of Celtis species using random forest with infrared spectroscopy and analysis of spectral feature importance, The Korean Data & Information Science Society 32(6):1183-1194 (2021). 10.7465/jkdi.2021.32.6.1183
27
Hwang, S. W., Hwang, U. T., Jo, K., Lee, T., Park, J., Kim, J. C., Kwak, H. W., Choi, I. G., and Yeo, H., NIR-chemometric approaches for evaluating carbonization characteristics of hydrothermally carbonized lignin, Scientific Reports 11(1):16979 (2021). 10.1038/s41598-021-96461-x34417504PMC8379198
28
Gaitán-Alvarez, J., Berrocal, A., Mantanis, G. I., Moya, R., and Araya, F., Acetylation of tropical hardwood species from forest plantations in Costa Rica: an FTIR spectroscopic analysis, Journal of Wood Science, 66(1):49 (2020). 10.1186/s10086-020-01898-9
29
Faix, O., Classification of lignins from different botanical origins by FT-IR spectroscopy, Holzforschung 45(s1):21-27 (1991). 10.1515/hfsg.1991.45.s1.21
30
Collier, W. E., Schultz, T. P., and Kalasinsky, V. F., Infrared study of lignin: reexamination of aryl-alkyl ether C-O stretching peak assignments, Holzforschung 46(6):523-528 (1992). 10.1515/hfsg.1992.46.6.523
31
Schwanninger, M., Stefke, B., and Hinterstoisser, B., Qualitative assessment of acetylated wood with infrared spectroscopic methods, Journal of Near Infrared Spectroscopy 19(5):349-357 (2011). 10.1255/jnirs.942
32
Zini, E., Scandola, M., and Gatenholm, P., Heterogeneous acylation of flax fibers, Reaction kinetics and surface properties, Biomacromolecules 4(3):821-827 (2003). 10.1021/bm034040h12741804
33
Yan, C. A. O., Zhang, J., Jiasong, H. E., Huiquan, L. I., and Zhang, Y., Homogeneous acetylation of cellulose at relatively high concentrations in an ionic liquid, Chinese Journal of Chemical Engineering, 18(3):515-522 (2010). 10.1016/S1004-9541(10)60252-2
34
Lee, Y. J., A Study on the quality characterization of printing and writing paper using surface parameters and data mining techniques, Master thesis, Kookmin University, Korea (2022).
35
Domingos, P., Occam's Two Razors: The Sharp and the Blunt. In: Agrawal, R., Stolorz, P., Piatetsky-Shapiro, G. (eds.) The Proceedings of the Fourth International Conference on Knowledge Discovery & Data Mining, New York City, August 27-31 (1998).
36
Hwang, S. W., Park, G. Y., Kim, J. H., and Jeong, M. J., Predictive modeling of traditional Korean paper characteristics using machine learning approaches (part 1): discriminating manufacturing origins with artificial neural networks and infrared spectroscopy, Journal of Korea TAPPI 55(4):57-69 (2023). 10.7584/JKTAPPI.2023.8.55.4.57
Information
  • Publisher :Korea Technical Association of The Pulp and Paper Industry
  • Publisher(Ko) :한국펄프종이공학회
  • Journal Title :Journal of Korea TAPPI
  • Journal Title(Ko) :펄프종이기술
  • Volume : 55
  • No :5
  • Pages :83-95
  • Received Date : 2023-09-23
  • Revised Date : 2023-10-24
  • Accepted Date : 2023-10-26
  • A Corrigendum to this article was published on 30 December 2023.
    This article has been updated.
  • DOI :https://doi.org/10.7584/JKTAPPI.2023.10.55.5.83
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