Development of an alternative kombucha drink from gilaburu juice: Gilaburu-flavoured kombucha

Year 2024, Volume: 42 Issue: 3, 805 - 813, 12.06.2024

Berfin Eroğlu , Eda Delik , Burcu Emine Tefon Öztürk

Abstract

Kombucha is a Far East-originated fermented beverage that is valued by people for its health benefits worldwide. Fruit and fruit juice of the gilaburu plant is used in traditional medicine. In this study, fruit juice of gilaburu is used in a 21-day kombucha fermentation. Sensory properties, microbiological profile, total phenolic and flavonoid content, and antioxidant and antibacterial activity of the prepared beverages for days 0, 7, 14, and 21st were investigated. In addition, chemical compound profiles were determined using LC-MS/MS on the 7th and 14th days. When evaluated in general, gilaburu-flavoured kombucha yielded better results than traditional kombucha in terms of microbiological analysis, antibacterial activity, total phenolic content, and sensory properties. In addition, the LC-MS/MS analysis of the beverages revealed fourteen active compounds. Notably, on the 7th day, gilaburu-flavoured kombucha exhibited elevated levels of fumaric acid, quinic acid, chlorogenic acid, catechin, 4-OH-benzoic acid, epicatechin, vitexin and hesperidin compared to traditional kombucha.

Keywords

Antibacterial, Antioxidant, Flavonoid and Phenolic, Kombucha, Microbiological Profile, Sensory Feature

References

• [1] World Health Organization. Coronavirus disease (COVID-2019) situation reports. Available at: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports. Accessed on May 7, 2024.
• [2] Wu JT, Leung K, Leung GM. Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: A modelling study. Lancet 2020;395:689697. [CrossRef]
• [3] Giordano G, Blanchini F, Bruno R, Colaneri P, Di Filippo A, Di Matteo A, et al. Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy. Nat Med 2020;26(6):855860.
• [4] Maier BF, Brockmann D. Effective containment explains subexponential growth in recent confirmed COVID-19 cases in China. Science 2020;368:742746. [CrossRef]
• [5] Anastassopoulou C, Russo L, Tsakris A, Siettos C. Data-based analysis, modelling and forecasting of the COVID-19 outbreak. PLoS One 2020;15:e0230405. [CrossRef]
• [6] Turkyilmazoglu M. Explicit formulae for the peak time of an epidemic from the SIR model. Physica D 2021;422:132902. [CrossRef]
• [7] Turkyilmazoglu M. An extended epidemic model with vaccination: Weak-immune SIRVI. Physica A 2022;598:127429.
• [8] Turkyilmazoglu M. A restricted epidemic SIR model with elementary solutions. Phsysica A 2022;600:127570. [CrossRef]
• [9] Tunc H, Sari M, Kotil SE. Effect of sojourn time distributions on the early dynamics of COVID-19 outbreak. Nonlinear Dyn 2023;111:1168511702. [CrossRef]
• [10] Saba AI, Elsheikh AH. Forecasting the prevalence of COVID-19 outbreak in Egypt using nonlinear autoregressive artificial neural networks. Process Saf Environ Prot 2020;141:18. [CrossRef]
• [11] Ardabili SF, Mosavi A, Ghamisi P, Ferdinand F, Varkonyi-Koczy AR, Reuter U, et al. COVID-19 outbreak prediction with machine learning. Available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3580188. Accessed on May 7, 2024.
• [12] Benvenuto D, Giovanetti M, Vassallo L, Angeletti S, Ciccozzi M. Application of the ARIMA model on the COVID-2019 epidemic dataset. Data Brief 2020;29:105340. [CrossRef]
• [13] Al-Qaness MAA, Ewees AA, Fan H, Abd El Aziz M. Optimization method for forecasting confirmed cases of COVID-19 in China. J Clin Med 2020;9:674. [CrossRef]
• [14] Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Rajendra Acharya U. Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Med 2020;121:103792. [CrossRef]
• [15] Ceylan Z. Estimation of COVID-19 prevalence in Italy, Spain, and France. Sci Total Environ 2020;729:138817. [CrossRef]
• [16] Bapir SY, Kareem SM. COVID-19 and functionality: By providing social distancing of indoor common spaces in residental building. J Stud Sci Eng 2021;1:3645. [CrossRef]
• [17] Xue H, Bai Y, Hu H, Liang H. Influenza activity surveillance based on multiple regression model and artificial neural network. IEEE Access 2017;6:563575. [CrossRef]
• [18] Liu X, Jiang B, Gu W, Liu Q. Temporal trend and climate factors of hemorrhagic fever with renal syndrome epidemic in Shenyang City, China. BMC Infect Dis 2011;11:331. [CrossRef]
• [19] Balkhy HH, Abolfotouh MA, Al-Hathlool RH, Al-Jumah MA. Awareness, attitudes, and practices related to the swine influenza pandemic among the Saudi public. BMC Infect Dis 2010;10:42. [CrossRef]
• [20] Ahmad S, Mehfuz S, Mebarek-Oudina F, Beg J. RSM analysis based cloud access security broker: A systematic literature review. Cluster Comput 2022;25:37333763. [CrossRef]
• [21] Guan P, Huang DS, Zhou BS. Forecasting model for the incidence of hepatitis A based on artificial neural network. World J Gastroenterol 2004;10:35793582. [CrossRef]
• [22] Faisal T, Taib MN, Ibrahim F. Neural network diagnostic system for dengue patients risk classification. J Med Syst 2012;36:661–676. [CrossRef]
• [23] Elveren E, Yumuşak N. Tuberculosis disease diagnosis using artificial neural network trained with genetic algorithm. J Med Syst 2011;35:329332. [CrossRef]
• [24] Baridam B, Irozuru C. The prediction of prevalence and spread of HIV/AIDS using artificial neural network–the case of rivers State in the Niger Delta, Nigeria. Int J Comput Appl 2012;44:42–45. [CrossRef]
• [25] Aburas HM, Cetiner BG, Sari M. Dengue confirmed-cases prediction: A neural network model. Expert Syst Appl 2010;37:42564260. [CrossRef]
• [26] Koliopoulos TK, Papakonstantinou D, Ciarkowska K, Antonkiewicz J, Gambus F, Mebarek-Oudina F, et al. Green Designs in Hydraulics - Construction Infrastructures for Safe Agricultural Tourism and Sustainable Sports Tourism Facilities Mitigating Risks of Tourism in Crisis at Post COVID-19 Era. In: de Carvalho JV, Liberato P, Peña A, editors. Advances in Tourism, Technology and Systems. Smart Innovation, Systems and Technologies. 2nd ed. New York: Springer; 2022. p. 3747. [CrossRef]
• [27] Chaurasia V, Pal S. COVID-19 pandemic: ARIMA and regression model-based worldwide death cases predictions. SN Comput Sci 2020;1:288. [CrossRef]
• [28] Amar LA, Taha AA, Mohamed MY. Prediction of the final size for COVID-19 epidemic using machine learning: A case study of Egypt. Infect Dis Model 2020;5:622634. [CrossRef]
• [29] Niazkar HR, Niazkar M. Application of artificial neural networks to predict the COVID-19 outbreak. Glob Health Res Policy 2020;5:50. [CrossRef]
• [30] Melin P, Monica JC, Sanchez D, Castillo O. Multiple ensemble neural network models with fuzzy response aggregation for predicting COVID-19 time series: The case of mexico. Healthcare (Basel) 2020;8:181. [CrossRef]
• [31] Tamang SK, Singh PD, Datta B. Forecasting of Covid-19 cases based on prediction using artificial neural network curve fitting technique. Glob J Environ Sci Manag 2020;6:5364.
• [32] Moftakhar L, Seif M, Safe MS. Exponentially increasing trend of infected patients with COVID-19 in Iran: A comparison of neural network and arima forecasting models. Iran J Public Health 2020;49:92100. [CrossRef]
• [33] Nakip M, Copur O, Guzelis C. Comparative study of forecasting models for COVID-19 outbreak in Turkey. Available at: https://www.iitis.pl/sites/default/files/pubs/Covid_19_Forecasting%20%281%29.pdf. Accessed on May 7, 2024.
• [34] Toğa G, Atalay B, Toksari MD. COVID-19 prevalence forecasting using Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN): Case of Turkey. J Infect Public Health 2021;14:811816. [CrossRef]
• [35] Guleryuz D. Forecasting outbreak of COVID-19 in Turkey; Comparison of Box-Jenkins, Brown's exponential smoothing and long short-term memory models. Process Saf Environ Prot 2021;149:927935. [CrossRef]
• [36] Demir E, Canitez MN, Elazab M, Hameed AA, Jamil A, Al-Dulaimi AA. Assessing the spreading behavior of the Covid-19 epidemic: A case study of Turkey. Available at: https://acikerisim.istinye.edu.tr/xmlui/handle/20.500.12713/3242. Accessed on May 7, 2024.
• [37] Kuvvetli Y, Deveci M, Paksoy, T, Garg H. A predictive analytics model for COVID-19 pandemic using artificial neural networks. Decis Anal J 2021;1:100007. [CrossRef]
• [38] Caglar O, Ozen F. A comparison of Covid-19 cases and deaths in Turkey and in other countries. Netw Model Anal Health Inform Bioinform 2022;11:45. [CrossRef]
• [39] Kırbaş İ, Sözen A, Tuncer AD, Kazancıoğlu FŞ. Comparative analysis and forecasting of COVID-19 cases in various European countries with ARIMA, NARNN and LSTM approaches. Chaos Solitons Fractals 2020;138:110015. [CrossRef]
• [40] Weisberg S. Applied Linear Regression. 3th ed. New Jersey: John Wiley & Sons, Inc; 2005. [CrossRef]
• [41] Fox J. Applied Regression Analysis: Linear Models and Related Methods. California: SagePublication; 1997.
• [42] Skapura DM. Building Neural Networks. 1st ed. Boston: Addison-Wesley; 1995.
• [43] Haykin S. Neural Networks: A Comprehensive Foundation. New York: Macmillian College Publishing Company Inc; 1994.
• [44] Chaudhuri BB, Bhattacharya U. Efficient training and improved performance of multilayer perceptron in pattern classification. Neurocomput 2000;34:1127. [CrossRef]
• [45] PRT-DTO, 2020. Presidency of The Republic of Turkey Digital Transformation Office. Available at: https://cbddo.gov.tr/ Accessed on May 7, 2024.
• [46] T.C Sağlık Bakanlığı – Covid-19 Bilgilendirme Platformu. Günlük Covid-19 aşı tablosu. Available at: https://covid19.saglik.gov.tr/. Accessed on May 7, 2024.
• [47] Zhang G, Patuwo BE, Hu MY. Forecasting with artificial neural networks: The state of the art. Int J Forecast 1998;14:3562. [CrossRef]
• [48] Spangler WE, May JH, Vargas LG. Choosing datamining for multiple classification: Representational and performance measurement implications for decision support. J Manag Inf Syst 1999;16:3762. [CrossRef]
• [49] Uysal M, Roubi SE. Artificial neural networks versus multiple regression in tourism demand analysis. J Travel Res 1999;38:111118. [CrossRef]
• [50] Fadlalla A, Lin CH. An analysis of the applications of neural networks in finance. Interfaces 2001;31:112122. [CrossRef]
• [51] Nguyen N, Cripps A. Predicting housing value: a comparison of multiple regression analysis and artificial neural networks. J Real Estate Res 2001;22:313336. [CrossRef]
• [52] Gorr WL. Research prospective on neural network forecasting. Int J Forecast 1994;10:14. [CrossRef]
• [53] Hill T, Remus W. Neural network approach for intelligent support of managerial decision making. Decis Support Syst 1994;11:449459. [CrossRef]
• [54] Wray B, Palmer A, Bejou D. Using neural network analysis to evaluate buyer-seller relationship. Eur J Market 1994;28:3248. [CrossRef]