Journal of Korea TAPPI ISSN:0253-3200(Print) 펄프종이기술

Schützenberger, P., Action de l'acide acétique anhydre sur la cellulose, l'amidon, les sucres, la mannite et ses congénères, les glucosides et certaines matières colorantes végétales, Compt Rend Hebd Séances Acad Sci 61:484-487 (1865).

Meade, R. K., McCormack H., Clark, L. T., Sclater, A. G., and Lamborn, L., Chemical Engineer: A Monthly Journal of Practical, Applied and Analytical Chemistry: McCready Publishing Company (1905).

Sjostrom, E., Wood chemistry: fundamentals and applications, Elsevier (2013).

Rodrigues Filho, G., Monteiro, D. S., da Silva Meireles, C., de Assunção, R. M. N., Cerqueira, D. A., Barud, H. S., Ribeiro, S. J. L., and Messadeq, Y., Synthesis and characterization of cellulose acetate produced from recycled newspaper, Carbohydrate Polymers 73(1):74-82 (2008).

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).

Cai, J., Zhou, R., Li, T., He, J., Wang, G., Wang, H., and Xiong, H., Bamboo cellulose-derived cellulose acetate for electrospun nanofibers: synthesis, characterization and kinetics, Cellulose 25:391-398 (2018).

Mukherjee, K., Agarwal, R. K., Chaturvedi, H. K., and Gupta, B. D., Water soluble cellulose acetate and its application (1981).

Liu, H. and Hsieh, Y. L., Ultrafine fibrous cellulose membranes from electrospinning of cellulose acetate, Journal of Polymer Science Part B: Polymer Physics 40(18):2119-2129 (2002).

Yu, I., Iwata, K., Ueda, K., and Nakayama, H., Molecular modeling of the solvent structuring of chloroform around cellulose triacetate, Bulletin of the Chemical Society of Japan 76(12):2285-2292 (2003).

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 8:363-370 (1961).

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(43):6194-6201 (2017).

Wolfs, J., Scheelje, F. C. M., Matveyeva, O., and Meier, M. A., Determination of the degree of substitution of cellulose esters via ATR‐FTIR spectroscopy, Journal of Polymer Science 61(21):2697-2707 (2023).

Freire, C. S. R., Silvestre, A. J. D., Pascoal Neto, C., and Rocha, R. M. A., An efficient method for determination of the degree of substitution of cellulose esters of long chain aliphatic acids, Cellulose 12:449-458 (2005).

Casarano, R., Fidale, L. C., Lucheti, C. M., Heinze, T., and El Seoud, O. A., Expedient, accurate methods for the determination of the degree of substitution of cellulose carboxylic esters: Application of UV-vis spectroscopy (dye solvatochromism) and FTIR, Carbohydrate Polymers 83(3):1285-1292 (2011).

Vaca-Garcia, C., Borredon, M. E., and Gaseta, A., Determination of the degree of substitution (DS) of mixed cellulose esters by elemental analysis, Cellulose 8:225-231 (2001).

Kim, K. J., Kim, J. H., Park, G., and Jeong, M. J., Predictive modeling of korean traditional paper characteristics using machine learning approaches (Part 2): prediction of carbonyl content and analysis of variable importance using random forest, Journal of Korea TAPPI 55(5):13-23 (2023).

Lee, Y. J., Kweon, S. W., Jeong, C. W., and Kim, H. J., Evaluating the performance of machine learning and variable selection methods to identify document paper using infrared spectral data, Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 327:125299 (2025).

Hwang, S. W., Chung, H., Lee, T., Kwak, H. W., Choi, I. G., and Yeo, H., Investigation of NIR spectroscopy and electrical resistance-based approaches for moisture determination of logging residues and sweet sorghum, BioResources 18(1):2064-2082 (2023).

Lee, Y. J., Lee, J. E., Gwon, J. G., Lee, T. J., and Kim, H. J., Predictive modeling for degree of substitution of cellulose acetate using infrared spectroscopy and machine learning, Journal of Korea TAPPI 55(5):83-95 (2023).

Savitzky, A. and Golay, M. J., Smoothing and differentiation of data by simplified least squares procedures, Analytical Chemistry 36(8):1627-1639 (1964).

Hwang, S. W., Park, G., Kim, J., Kang, K. H., and Lee, W. H., One-dimensional convolutional neural networks with infrared spectroscopy for classifying the origin of printing paper, BioResources 19(1):1633-1651 (2024).

Kim, K. J. and Eom, T. J., Study on the aging characteristics of paper with principal component analysis, Journal of Korea TAPPI 48(6):144-149 (2016).

Kim, J., Kang, K. H., and Kim, K. J., A study on the classification of European copy papers with infrared spectroscopy and principal component analysis, Journal of Korea TAPPI 54(4):31-41 (2022).

Breiman, L., Machine Learning, Random forests 45(1):5-32 (2001).

Breiman, L., Bagging predictors, Machine Learning, 24:123-140 (1996).

Breiman, L., Classification and regression trees, Routledge (2017).

Louppe, G., Wehenkel, L., Sutera, A., and Geurts, P., Understanding variable importances in forests of randomized trees, Advances in Neural Information Processing Systems 26 (2013).

Garside, P. and Wyeth, P., Identification of cellulosic fibres by FTIR spectroscopy-thread and single fibre analysis by attenuated total reflectance, Studies in Conservation 48(4):269-275 (2003).

Lee, Y. J., Won, S. Y., Park, S. B., and Kim, H. J., Chemometric approaches for discriminating manufacturers of Korean handmade paper using infrared spectroscopy, Heritage Science 12(1):1-12 (2024).

Frisoni, G., Baiardo, M., Scandola, M., Lednická, D., Cnockaert, M. C., Mergaert, J., and Swings, J., Natural cellulose fibers: heterogeneous acetylation kinetics and biodegradation behavior, Biomacromolecules 2(2):476-482 (2001).

Tosh, B., Saikia, C. N., and Dass, N. N., Homogeneous esterification of cellulose in the lithium chloride-N, N-dimethylacetamide solvent system: effect of temperature and catalyst, Carbohydrate Research 327(3):345-352 (2000).

Salim, M. R., Yaacob, M., Ibrahim, M. H., Azmi, A. I., Ngajikin, N. H., Dooly, G., and Lewis, E., An optical spectroscopic based reflective sensor for CO₂ measurement with signal to noise ratio improvement, Journal of Optoelectronics and Advanced Materials 17(5-6):519-525 (2015).

Hwang, S. W., Park, G., Kim, J., 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).

Hwang, S. W., Chung, H., Lee, T., Kim, J., Kim, Y., Kim, J. C., Kwak H. W., Choi, I., 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 (2023).

Horikawa, Y., Hirano, S., Mihashi, A., Kobayashi, Y., Zhai, S., and Sugiyama, J., Prediction of lignin contents from infrared spectroscopy: chemical digestion and lignin/biomass ratios of Cryptomeria japonica, Applied biochemistry and biotechnology 188:1066-1076 (2019).