Skip to main content

Optimization of extraction conditions of flavonoids from celery seed using response surface methodology


Celery seed is a potential medicinal material for the development of anti-gout agents because of its uric acid-lowering, xanthine oxidase inhibitory, anti-inflammatory and analgesic effects. In this study, the process of extraction of flavonoid from celery seed was optimized using response surface methodology. Various algorithms were used in order to model the relationship between output response (yield of extraction, total flavonoid content) and input parameters (ethanol concentration, temperature and solvent/solid ratio). The best model was established using multiple linear regression (MLR) algorithm. The optimal conditions were determined as follows: ethanol concentration: 90%; temperature: 90°C and the solvent/solid ratio: 4 ml/g. At these conditions, the yield of extraction was 11.57 ± 1.46% and the total flavonoid content was 4.922 ± 0.312 mg/g. The xanthine oxidase inhibitory effect of apigenin - the main flavonoid of celery seed, was evaluated with the IC50 value of 0.247 µg/ml (95% CI 0.129–0.617 µg/ml). It was suggested that flavonoid might play a key role in the xanthine oxidase inhibitory activity of celery seed extract.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2


  1. M. Gliozzi, N. Malara, S. Muscoli, V. Mollace, Int. J. Cardiol. 213, 23–27 (2016)

    Article  Google Scholar 

  2. P. Pacher, A. Nivorozhkin, C. Szabó, Pharmacol. Rev. 58, 87–114 (2006)

    Article  CAS  Google Scholar 

  3. N.T. Duong, N.T. Hang, V.T.P. Thao, T.T.B. Ngoc, J. Med. Mater. 19, 303–306 (2014)

    Google Scholar 

  4. N.T. Hang, N.T. Duong, N.T. Tung, Vietnam J. Pharm. 54, 67–71 (2014)

    Google Scholar 

  5. L.-Z. Lin, S. Lu, J.M. Harnly, J. Agric. Food Chem. 55, 1321–1326 (2007)

    Article  CAS  Google Scholar 

  6. R. Injac, M. Boskovic, N. Kocevar, T. Vovk, Anal. Chim. Acta 620, 150–161 (2008)

    Article  CAS  Google Scholar 

  7. V. Czitrom, Am. Stat. 53, 126–131 (1999)

    Google Scholar 

  8. C. Liyana-Pathirana, F. Shahidi, Food Chem. 93, 47–56 (2005)

    Article  CAS  Google Scholar 

  9. V. Vapnik, I. Guyon, T. Hastie, Mach. Learn 20, 273–297 (1995)

    Google Scholar 

  10. S.M. Satapathy, M. Kumar, S.K. Rath, C.S.I. Trans, ICT 1, 367–380 (2013)

    Google Scholar 

  11. G. Guo, H. Wang, D. Bell, Y. Bi, K. Greer, On the Move to Meaningful Internet Systems (Springer, 2003), pp. 986–996.

  12. D.C. Park, M.A. El-Sharkawi, R.J. Marks, L.E. Atlas, M.J. Damborg, IEEE Trans. Power Syst. 6, 442–449 (1991)

    Article  Google Scholar 

  13. T. Noro, Y. Oda, T. Miyase, A. Ueno, S. Fukushima, Chem. Pharm. Bull. 31, 3984–3987 (1983)

    Article  CAS  Google Scholar 

  14. M.T.T. Nguyen, S. Awale, Y. Tezuka, Q. Le Tran, H. Watanabe, S. Kadota, Biol. Pharm. Bull. 27, 1414–1421 (2004)

    Article  CAS  Google Scholar 

  15. K.E. Hevener, W. Zhao, D.M. Ball, K. Babaoglu, J. Qi, S.W. White, R.E. Lee, J. Chem. Inf. Model. 49, 444–460 (2009)

    Article  CAS  Google Scholar 

  16. R. Darnag, B. Minaoui, M. Fakir, Arab. J. Chem. 10, S600–S608 (2017)

    Article  CAS  Google Scholar 

  17. A.R. Tapas, D.M. Sakarkar, R.B. Kakde, Trop. J. Pharm. Res. 7, 1089–1099 (2008)

    Article  Google Scholar 

  18. D. Kashyap, A. Sharma, H.S. Tuli, K. Sak, V.K. Garg, H.S. Buttar, W.N. Setzer, G. Sethi, J. Funct. Foods 48, 457–471 (2018)

    Article  CAS  Google Scholar 

  19. C.-M. Lin, C.-S. Chen, C.-T. Chen, Y.-C. Liang, J.-K. Lin, Biochem. Biophys. Res. Commun. 294, 167–172 (2002)

    Article  CAS  Google Scholar 

  20. M.R. de Souza, C.A. de Paula, M.L.P. de Resende, A. Grabe-Guimarães, J.D. de Souza Filho, D.A. Saúde-Guimarães, J. Ethnopharmacol. 142, 845–850 (2012)

    Article  Google Scholar 

  21. R.-R. Li, L.-L. Pang, Q. Du, Y. Shi, W.-J. Dai, K.-S. Yin, Immunopharmacol. Immunotoxicol. 32, 364–370 (2010)

    Article  CAS  Google Scholar 

  22. A. Russo, R. Acquaviva, A. Campisi, V. Sorrenti, C. Di Giacomo, G. Virgata, M.L. Barcellona, A. Vanella, Cell Biol. Toxicol. 16, 91–99 (2000)

    Article  CAS  Google Scholar 

Download references


This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Phuong Nguyen Van.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.



See Figs. 3, 4, 5, 6 and Tables 4, 5, 6.

Fig. 3
figure 3

RMSE of kNN models for yield of extraction (Y1) with different values of k

Fig. 4
figure 4

RMSE of kNN models for total flavonoid content (Y2) with different values of k

Fig. 5
figure 5

Desirability plot for optimization of the process of extraction of flavonoid from celery seed

Fig. 6
figure 6

Superposition of the docking pose and the complexed ligand (RMSD = 0.5263 Å)

Table 4 ANOVA table of MLR model for yield of extraction (Y1)
Table 5 ANOVA table of MLR model for total flavonoid content (Y2)
Table 6 RMSE of ANNs with different number of neurons in hidden layer

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Nguyen Thu, H., Nguyen Van, P., Ngo Minh, K. et al. Optimization of extraction conditions of flavonoids from celery seed using response surface methodology. Food Measure 15, 134–143 (2021).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


  • Apium graveolens L.
  • Celery seed
  • Flavonoid
  • Response surface methodology
  • Xanthine oxidase inhibitor