AbstractIntroduction. This article presents the development of mathematical models related to the effect of the initial content of dry matter, yeast, and yeast energizer on the fermentation rate, the alcohol content, and the dry matter content in the finished product – mead.
Study objects and methods. The mathematical models were developed by using the response surface methodology (RSM). The effect of yeast, dry matter, and yeast energizer contents were tested in concentration ranges of 150–600 mg/L, 16.3–24.4%, and 140–500 mg/L, respectively. The starting substrates used were honeydew honey and 10% apple juice. Yeast was rehydrated and added in different amounts to obtain required concentrations. Initial dry matter concentrations were measured by a refractometer. At the end of fermentation, oenological parameters of mead, namely dry matter content, pH, and ethanol yield, were determined according to standard methods.
Results and discussion. The statistical estimation of the developed models and the individual model parameters showed that the initial dry matter content had a significant effect on the content of alcohol and dry matter in the final product. While, the initial content of yeast and yeast energizer did not have a significant effect in the tested concentration ranges. In addition, it was proved that the initial content of dry matter and yeast energizer had a significant effect on the fermentation rate, i.e. on the course of fermentation, which was described by a second-degree polynomial.
Conclusion. We determined the optimum content of dry matter (24.4%), amount of yeast (150 mg/L), and concentration of yeast energizer (140 mg/L) in the initial raw material which provided the maximum alcohol yield at a consistent fermentation rate.
KeywordsResponse surface methodology, mathematical models, fermentation, mead, yeast
FUNDINGThis study is a result of the research conducted within the Project (19/6-020/961-68/18) financially supported by the Ministry for Scientific and Technological Development, Higher Education and Information Society of the Republic of Srpska.
- Khuri AI. A general overview of response surface methodology. Biometrics and Biostatistics International Journal. 2017;5(3):87–93. https://doi.org/10.15406/bbij.2017.05.00133.
- Response surface designs [Internet]. [cited 2021 Aug 10]. Available from: https://www.statease.com/docs/v12/designs/rsm/#rsm.
- Myers RH, Montgomery DC. Response surface methodology: process and product optimization using designed experiments. New York: Wiley, 1995. 700 p.
- Mumtaz MW, Adnan A, Mukhtar H, Rashid U, Danish M. Biodiesel production through chemical and biochemical transesterification: Trends, technicalities, and future perspectives. In: Rasul MG, Azad Akalam, Sharma SC, ediitors. Clean energy for sustainable development: Comparisons and contrasts of new approaches. Academic Press; 2017, pp. 465–485. https://doi.org/10.1016/B978-0-12-805423-9.00015-6.
- Jang S, Lee AY, Lee AR, Choi G, Kim HK. Optimization of ultrasound-assisted extraction of glycyrrhizic acid from licorice using response surface methodology. Integrative Medicine Research. 2017;6(4):388–394. https://doi.org/10.1016/j.imr.2017.08.003.
- Ghelicha R, Jahannama MR, Abdizadeh H, Torknik FS, Vaezic MR. Central composite design (CCD)-Response surface methodology (RSM) of effective electrospinning parameters on PVP-B-Hf hybrid nanofibrous composites for synthesis of HfB2-based composite nanofibers. Composites Part B: Engineering. 2019;166:527–541. https://doi.org/10.1016/j.compositesb.2019.01.094.
- Şenaras AE. Parameter optimization using the surface response technique in automated guided vehicles. In: Kumar K, Zindani D, Davim P, editors. Sustainable engineering products and manufacturing technologies. Academic Press; 2019. pp. 187–197. https://doi.org/10.1016/B978-0-12-816564-5.00008-6.
- Miladinović MR, Stamenković OS, Banković PT, Milutinović-Nikolić AD, Jovanović DM, Veljković VB. Modeling and optimization of sunflower oil methanolysis over quicklime bits in a packed bed tubular reactor using the response surface methodology. Energy Conversion and Management. 2016;130:25–33. https://doi.org/10.1016/j.enconman.2016.10.020.
- Humbrid D, Fei Q. Scale-up considerations for biofuels. In: Eckert CA, Trinh CT. Biotechnology for biofuel production and optimization. Elseiver; 2016. pp. 513–537. https://doi.org/10.1016/B978-0-444-63475-7.00020-0.
- Schwarz LV, Marcon AR, Delamare APL, Echeverrigaray S. Influence of nitrogen, minerals and vitamins supplementation on honey wine production using response surface methodology. Journal of Apicultural Research. 2020;60(3):57–66. https://doi.org/10.1080/00218839.2020.1793277.
- Srimeena N, Gunasekaran S, Murugesan R. Optimizing process conditions for stingless bee (Melipona irridipennis) mead fermentation using plackett-burman design and response surface methodology. Asian Journal of Chemistry. 2016;28(1):143–150. https://doi.org/10.14233/ajchem.2016.19280.
- Pereira AP, Mendes-Ferreira A, Oliveira JM, Estevinho LM, Mendes-Faia A. High-cell-density fermentation of Saccharomyces cerevisiae for the optimization of mead production. Food Microbiology. 2013;33(1):114–123. https://doi.org/10.1016/j.fm.2012.09.006.
- Gomes T, Barradas C, Dias T, Verdial J, Morais JS, Ramalhos E, et al. Optimization of mead production using Response Surface Methodology. Food and Chemical Toxicology. 2013;59:680–686. https://doi.org/10.1016/j.fct.2013.06.034.
- Chitarrini G, Debiasi L, Stuffer M, Ueberegger E, Zehetner E, Jaeger H, et al. Volatile profile of mead fermenting blossom honey and honeydew honey with or without Ribes nigrum. Molecules. 2020;25(8). https://doi.org/10.3390/molecules25081818.
- Balogu TV, Towobola O. Production and quality analysis of wine from honey and coconut milk blend using Saccharomyces cerevisiae. Fermentation. 2017;3(2). https://doi.org/10.3390/fermentation3020016.
- Adamenko K, Kawa-Rygielska J, Kucharska AZ, Piórecki N. Characteristics of biologically active compounds in Cornelian cherry meads. Molecules. 2018;23(8). https://doi.org/10.3390/molecules23082024.
- Amorim TS, Lopes SDB, Bispo JAC, Bonafe CFS, de Carvalho GBM, Martínez EA. Influence of acerola pulp concentration on mead production by Saccharomyces cerevisiae AWRI 796. LWT. 2018;97:561–569. https://doi.org/10.1016/j.lwt.2018.07.009.
- Savić A, Velemir A, Papuga S, Stojković M. Influence of blackberry juice addition on mead fermentation and quality. Foods and Raw Materials. 2021;9(1):146–152. https://doi.org/10.21603/2308-4057-2021-1-146-152.
- Boyer J, Liu RH. Apple phytochemicals and their health benefits. Nutrition Journal. 2004;3. https://doi.org/10.1186/1475-2891-3-5.
- Compendium of international methods of wine and must analysis. Paris: Organisation Internationale de la Vigne et du Vin Paris; 2021.
- Savić A, Velemir A, Stojković M, Ilić P. Effect of correction of certain parameters of diluted honey on mead production. Proceedings: Conference of Chemists, Technologists and Environmentalists of Republic of Srpska. 2016;11:322–330.
- Martínez AM, Vivas GJ, Quicazán MC. Evaluation of alcoholic fermentation during the production of mead using immobilized cells in kappa-carrageenan. Chemical Engineering Transactions. 2016;49:19–24. https://doi.org/10.3303/CET1649004.
- Sroka P, Tuszyński T. Changes in organic acid contents during mead wort fermentation. Food Chemistry. 2007;104(3):1250–1257. https://doi.org/10.1016/j.foodchem.2007.01.046.
- Martínez-García R, García-Martínez T, Puig-Pujol A, Mauricio JC, Moreno J. Changes in sparkling wine aroma during the second fermentation under CO2 pressure in sealed bottle. Food Chemistry. 2017;237:1030–1040. https://doi.org/10.1016/j.foodchem.2017.06.066.
- Torrea D, Varela C, Ugliano M, Ancin-Azpilicueta C, Francis IL, Henschke PA. Comparison of inorganic and organic nitrogen supplementation of grape juice – Effect on volatile composition and aroma profile of a Chardonnay wine fermented with Saccharomyces cerevisiae yeast. Food Chemistry. 2011;127(3):1072–1083. https://doi.org/10.1016/j.foodchem.2011.01.092.