DOI QR코드

DOI QR Code

Microwave Radiation-Assisted Chitin Deacetylation: Optimization by Response Surface Methodology (RSM)

  • Iqmal Tahir (Department of Chemistry, Faculty of Mathematics and Natural Science, Universitas Gadjah Mada) ;
  • Karna Wijaya (Department of Chemistry, Faculty of Mathematics and Natural Science, Universitas Gadjah Mada) ;
  • Mudasir (Department of Chemistry, Faculty of Mathematics and Natural Science, Universitas Gadjah Mada) ;
  • Dita Krismayanti (Department of Chemistry, Faculty of Mathematics and Natural Science, Universitas Gadjah Mada) ;
  • Aldino Javier Saviola (Department of Chemistry, Faculty of Mathematics and Natural Science, Universitas Gadjah Mada) ;
  • Roswanira Abdul Wahab (Department of Chemistry, Universiti Teknologi Malaysia) ;
  • Amalia Kurnia Amin (Research Center for Chemistry, National Research and Innovation Agency (BRIN)) ;
  • Wahyu Dita Saputri (Research Center for Quantum Physics, National Research and Innovation Agency (BRIN)) ;
  • Remi Ayu Pratika (Study Program of Chemistry, Faculty of Mathematics and Natural Science, Universitas Palangka Raya)
  • 투고 : 2023.11.24
  • 심사 : 2024.01.26
  • 발행 : 2024.02.27

초록

The optimization of deacetylation process parameters for producing chitosan from isolated chitin shrimp shell waste was investigated using response surface methodology with central composite design (RSM-CCD). Three independent variables viz, NaOH concentration (X1), radiation power (X2), and reaction time (X3) were examined to determine their respective effects on the degree of deacetylation (DD). The DD of chitosan was also calculated using the baseline approach of the Fourier Transform Infrared (FTIR) spectra of the yields. RSM-CCD analysis showed that the optimal chitosan DD value of 96.45 % was obtained at an optimized condition of 63.41 % (w/v) NaOH concentration, 227.28 W radiation power, and 3.34 min deacetylation reaction. The DD was strongly controlled by NaOH concentration, irradiation power, and reaction duration. The coefficients of correlation were 0.257, 0.680, and 0.390, respectively. Because the procedure used microwave radiation absorption, radiation power had a substantial correlation of 0.600~0.800 compared to the two low variables, which were 0.200~0.400. This independently predicted robust quadratic model interaction has been validated for predicting the DD of chitin.

키워드

과제정보

This research was funded by the Faculty of Mathematics and Natural Sciences of UGM for providing funding assistance through the 2022 Lecturer Research Grant.

참고문헌

  1. N. Yan and X. Chen, Nature, 524, 155 (2015).
  2. S. N. Manjabhat, B. Narayan and M. N. Subbanna, J. Aquat. Food Prod. Technol., 15, 5 (2006).
  3. M. K. Rasweefali, S. Sabu, K. Muhammed Azad, M. K. Raseel Rahman, K. V. Sunooj, A. Sasidharan and K. K. Anoop, Adv. Biomarker Sci. Technol., 4, 12 (2022).
  4. E. Y. Wardhono, M. P. Pinem, I. Kustiningsih, M. Effendy, D. Clausse, K. Saleh and E. Guenin, Carbohydr. Polym., 267, 118180 (2021).
  5. C. Thambiliyagodage, M. Jayanetti, A. Mendis, G. Ekanayake, H. Liyanaarachchi and S. Vigneswaran, Materials, 16, 2073 (2023).
  6. M. Agarwal, M. K. Agarwal, N. Shrivastav, S. Pandey and P. Gaur, Int. J. Life-Sci. Sci. Res., 4, 1721 (2018).
  7. Y. W. Cho, J. Jang, C. R. Park and S. W. Ko, Biomacromolecules, 1, 609 (2000).
  8. M. Ghosh, S. Sadhukhan and K. K. Dey, Solid State Nucl. Magn. Reson., 97, 7 (2019).
  9. J. C. Roy, F. Salaun, S. Giraud, A. Ferri, G. Chen and J. Guan, in Solubility of Polysaccharides. ed. Z. Xu (Intech Open, London, UK, 2017) p.109.
  10. I. Younes and M. Rinaudo, Mar. Drugs, 13, 1133 (2015).
  11. V. Y. Novikov, I. N. Konovalova and N. V. Dolgopyatova, Appl. Biochem. Microbiol., 58, 309 (2022).
  12. A. E. Mamuk, C. Isik, A. Aslan and D. B. Altuntas, in Chitosan Nanocomposites. Biological and Medical Physics, Biomedical Engineering. ed. S. K. Swain, A. Biswal (Springer, Singapore 2023) p.255.
  13. B. Li, J. Elango and W. Wu, Appl. Sci., 10, 30 (2020).
  14. P. Wongpanit, N. Sanchavanakit, P. Pavasant, P. Supaphol, S. Tokura and R. Rujiravanit, Macromol. Biosci., 5, 1001 (2005).
  15. T. Hahn, J. Egger, S. Krake, M. Dyballa, L. Stegbauer, N. von Seggern, I. Bruheim and S. Zibek, J. Appl. Polym. Sci., 141, e54789 (2024).
  16. H. El Knidri, J. Dahmani, A. Addaou, A. Laajeb and A. Lahsini, Int. J. Biol. Macromol., 139, 1092 (2019).
  17. A. Sahu, P. Goswami and U. Bora, J. Mater. Sci.: Mater. Med., 20, 171 (2009).
  18. K. T. Hwang, S. T. Jung, G. D. Lee, M. S. Chinnan, Y. S. Park and H. J. Park, J. Agric. Food Chem., 50, 1876 (2002).
  19. R. F. Weska, J. M. Moura, L. M. Batista, J. Rizzi and L. A. A. Pinto, J. Food Eng., 80, 749 (2007).
  20. O. A. Olafadehan, T. O. Ajayi and K. O. Amoo, Theor. Found. Chem. Eng., 54, 1173 (2020).
  21. T. A. Khan, K. K. Peh and H. S. Ch'ng, J. Pharm. Pharm. Sci., 5, 205 (2002).
  22. J. G. Domszya and G. A. F. Roberts, Makromol. Chem., 186, 1671 (1985).
  23. L. Atmaja, H. Manimoy and L. E. Arizka, IPTEK J. Technol. Sci., 30, 95 (2019).
  24. I. Tahir, J. Millevania, K. Wijaya, Mudasir, R. A. Wahab and W. Kurniawati, Results Eng., 17, 100919 (2023).
  25. G. Derringer and R. Suich, J. Qual. Technol., 12, 214 (2018).