Mathematical modeling as a tool in kombucha beverages bioactive quality control

Original scientific paper

Authors

  • Jasmina Vitas University of Novi Sad, Faculty of Technology Novi Sad, Bulevar cara Lazara, 1, 21000 Novi Sad, Serbia https://orcid.org/0000-0002-6761-1880
  • Aleksandar Jokić University of Novi Sad, Faculty of Technology Novi Sad, Bulevar cara Lazara, 1, 21000 Novi Sad, Serbia https://orcid.org/0000-0001-6352-4512
  • Nataša Lukić University of Novi Sad, Faculty of Technology Novi Sad, Bulevar cara Lazara, 1, 21000 Novi Sad, Serbia https://orcid.org/0000-0003-1248-1238
  • Stefan Vukmanović University of Novi Sad, Faculty of Technology Novi Sad, Bulevar cara Lazara, 1, 21000 Novi Sad, Serbia https://orcid.org/0000-0002-0373-294X
  • Radomir Malbaša University of Novi Sad, Faculty of Technology Novi Sad, Bulevar cara Lazara, 1, 21000 Novi Sad, Serbia https://orcid.org/0000-0003-0230-4852

DOI:

https://doi.org/10.2298/CICEQ231229012V

Keywords:

winery effluent, total flavonoids, kombucha, sensory characteristics, response surface methodology

Abstract

This study examined the application of mathematical models on total flavonoids content and sensory mark of kombucha beverages on winery effluent. Process parameters were as follows: 0, 3, 6 and 9 days of fermentation time; 20, 25 and 30 °C of fermentation temperature and 3, 5 and 7% of initial total reducing sugars. Total flavonoids were determined spectrophotometrically and sensory mark by a descriptive test and five points category scale. Total flavonoids content decreased during the applied kombucha fermentation process, which lasted for 9 days. On average, total sensory mark suggested that consume kombucha products are obtained after 3 days of the fermentation, regardless on the fermentation temperature or sugars content. In order to produce kombucha beverage with the highest bioactive quality, response surface methodology proposed the following process parameters: 3 days of fermentation, 7% of initial total sugars and 30 °C process temperature.

References

S. Vukmanović, PhD Thesis, Faculty of Technology Novi Sad, University of Novi Sad (2022). https://nardus.mpn.gov.rs/handle/123456789/21144?locale-attribute=sr.

N. Abaci, F.S. Senol Deniz, I.E. Orhan, Food Chem.: X 14 (2022) 100302. https://doi.org/10.1016/j.fochx.2022.100302.

L.D.L. de Oliveira, M.V. de Carvalho, L. Melo, Rev. Ceres 61 (2014) 764-779. https://doi.org/10.1590/0034-737X201461000002.

M.M. Giusti, T.C. Wallace, In Handbook of Natural Colorants, T. Bechtold, R. Mussak, Eds., John Wiley & Sons, Ltd, Chichester, UK (2009), p. 255-275. https://doi.org/10.1002/9780470744970.ch16.

G. Kilic, I.Y. Sengun, Food Biosci. 53 (2023) 102631. https://doi.org/10.1016/j.fbio.2023.102631.

L.T. Phung, H. Kitwetcharoen, N. Chamnipa, N. Boonchot, S. Thanonkeo, P. Tippayawat, P. Klanrit, M. Yamada, P. Thanonkeo, Sci. Rep. 13 (2023) 7859. https://doi.org/10.1038/s41598-023-34954-7.

X. Wang, D. Wang, H. Wang, S. Jiao, J. Wu, Y. Hou, J. Sun, J. Yuan, LWT 168 (2022) 113931. https://doi.org/10.1016/j.lwt.2022.113931.

D. Cvetković, S. Markov, M. Djurić, D. Savić, A. Velićanski, J. Food Eng. 85 (2008) 387-392. https://doi.org/10.1016/j.jfoodeng.2007.07.021.

D. Cvetković, O. Šovljanski, A. Ranitović, A. Tomić, S. Markov, D. Savić, B. Danilović, L. Pezo: An artificial neural network as a tool for kombucha fermentation improvement, Chem. Ind. Chem. Eng. Q. 28 (2022) 277-286. https://doi.org/10.2298/CICEQ211013002C.

E. Lončar, M. Djurić, R. Malbaša, L.J. Kolarov, M. Klašnja, Food Bioprod. Process. 84 (2006) 186-192. https://doi.org/10.1205/fbp.04306.

R. Malbaša, L. Jevrić, E. Lončar, J. Vitas, S. Podunavac-Kuzmanović, S. Milanović, S. Kovačević, J. Food Sci. Technol. 52 (2015) 5968-5974. https://doi.org/10.1007/s13197-014-1648-4.

J. Vitas, R. Malbaša, A. Jokić, E.S. Lončar, S.D. Milanović, Mljekarstvo 68 (2018) 116-125. https://doi.org/10.15567/mljekarstvo.2018.0205.

R. Malbaša, E. Lončar, M. Djurić, M. Klašnja, L. J. Kolarov, S. Markov, Food Bioprod. Process 84 (2006) 193-199. https://doi.org/10.1205/fbp.05061.

F. Valiyan, H. Koohsari, A. Fadavi, J Food Sci Technol. 58 (2021) 1877-1891. http://doi:10.1007/s13197-020-04699-6

S. Vukmanović, J. Vitas, S. Kravić, Z. Stojanović, A. Đurović, B. Cvetković, R. Malbaša, Chem. Ind. Chem. Eng. Q. 00 (2024) 1-1. https://doi.org/10.2298/CICEQ231002001V.

J. Vitas, S. Vukmanović, R. Malbaša. Waste Biomass Valorization 14 (2023) 4187-4200. https://doi.org/10.1007/s12649-023-02130-7.

S. Vukmanović, J. Vitas, A. Ranitović, D. Cvetković, A. Tomić, R. Malbaša, LWT 154 (2022) 112726. https://doi.org/10.1016/j.lwt.2021.112726.

J. Vitas, S. Vukmanović, J. Čakarevic, L. Popović, R. Malbaša, Chem. Ind. Chem. Eng. Q. 26 (2020) 157-170. https://doi.org/10.2298/CICEQ190708034V.

J.S. Vitas, A.D. Cvetanović, P.Z. Mašković, J.V. Švarc-Gajić, R.V. Malbaša, J. Funct. Foods 44 (2018) 95-102. https://doi.org/10.1016/j.jff.2018.02.019.

A. Jokić, Z. Zavargo, Z. Šereš, M. Tekić, J. Membr. Sci. 350 (2010) 269-278. http://dx.doi.org/10.1016/j.memsci.2009.12.037.

S.A. Villarreal-Soto, S. Beaufort, J. Bouajila, J.-P. Souchard, P. Taillandier, J. Food Sci. 83 (2018) 580-588. https://doi.org/10.1111/1750-3841.14068.

R. Jayabalan, P. Subathradevi, S. Marimuthu, M. Sathishkumar, K. Swaminathan, Food Chem. 109 (2008) 227-234. https://doi.org/10.1016/j.foodchem.2007.12.037.

T.B.E. Öztürk, B. Eroğlu, E. Delik, M. Çiçek, E. Çiçek, Food Technol. Biotechnol. 61 (2023) 127-137. https://doi.org/10.17113/ftb.61.01.23.7789.

A. Braune, M. Blaut, Gut Microbes 7 (2016) 216-234. https://doi.org/10.1080/19490976.2016.1158395.

Y. Hsieh, M.-C. Chiu, J.-Y. Chou, J. Food Qual. 2021 (2021) 1735959. https://doi.org/10.1155/2021/1735959.

D. Hunaefi, D.N. Akumo, H. Riedel, I. Smetanska, Antioxidants 1 (2012) 4-32. https://doi.org/10.3390/antiox1010004.

H. Rodríguez, J.A. Curiel, J.M. Landete, B. de las Rivas, F. López de Felipe, C. Gómez-Cordovés, J.M. Mancheño, R. Muñoz, Int. J. Food Microbiol. 132 (2009) 79-90. https://doi.org/10.1016/j.ijfoodmicro.2009.03.025.

X. Pu, P. Ye, J. Sun, C. Zhao, X. Shi, B. Wang, W. Cheng, LWT 176 (2023) 114536. https://doi.org/10.1016/j.lwt.2023.114536.

Z. Liang, P. Zhang, W. Ma, X.-A. Zeng, Z. Fang, Food Biosci. 54 (2023) 102884. https://doi.org/10.1016/j.fbio.2023.102884.

S. Kumar, A.K. Pandey, Sci. World J. 2013 (2013) 162750. https://doi.org/10.1155/2013/162750.

G. Cohen, D.A. Sela, A.A. Nolden, Foods 12 (2023) 3116. https://doi.org/10.3390/foods12163116.

Downloads

Published

09.04.2024

Issue

Section

Articles

How to Cite

Mathematical modeling as a tool in kombucha beverages bioactive quality control: Original scientific paper. (2024). Chemical Industry & Chemical Engineering Quarterly. https://doi.org/10.2298/CICEQ231229012V

Funding data

Similar Articles

61-70 of 82

You may also start an advanced similarity search for this article.

Most read articles by the same author(s)