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Mixture modeling as a way for optimization of wort in beer production

• Georgi Kostov
• Vesela Shopska
• Rositsa Denkova-Kostova
• Kristina Ivanova

(2023). ECMS 2023, 37th Proceedings
Edited by: Enrico Vicario, Romeo Bandinelli, Virginia Fani, Michele Mastroianni, European Council for Modelling and Simulation.
DOI: http://doi.org/10.7148/2023
ISSN: 2522-2422 (ONLINE)
ISSN: 2522-2414 (PRINT)
ISSN: 2522-2430 (CD-ROM)
ISBN: 978-3-937436-80-7
ISBN: 978-3-937436-79-1 (CD) Communications of the ECMS Volume 37, Issue 1, June 2023, Florence, Italy June 20th – June 23rd, 2023

https://doi.org/10.7148/2023-0186

Georgi kostov, Vesela shopska, Rositsa denkova-kostova, Kristina ivanova (2023). Mixture modeling as a way for optimization of wort in beer production, ECMS 2023, Proceedings Edited by: Enrico Vicario, Romeo Bandinelli, Virginia Fani, Michele Mastroianni, European Council for Modelling and Simulation. doi:10.7148/2023-0186

Different malt types used in beer production are responsible not only for beer taste and aroma, but also for its biological value. In our previous research the main brewing and biological (phenolic compounds and antioxidant capacity) characteristics of 20 malt types, which are used in the brewing industry in Bulgaria, were studied. The aim of the present work is the modelling of a three-component mixture of malts (Pilsner, Caramel Munich type 2 and Vienna), in order to obtain wort with increased biological value.

The method for mixtures modelling was used, as the target functions were wort phenolic compounds, determined by Folin–Ciocalteu method and modified Glories method, and wort antioxidant potential, determined by DPPH radical scavenging assay and ferric reducing antioxidant power - FRAP. The proportions of the three malts were determined after ANOVA of the results obtained, in order to guarantee the maximum biological value of the wort.

The model obtained was optimized by applying constraints within the model in order to minimize the phenolic compounds content and maximize the antioxidant potential of wort produced. However, the optimization was carried out also to ensure that the wort produced showed good brewing characteristics. The obtained mixture had the following composition – 60% Pilsner, 20% Caramel Munnich type 2, and 20% Vienna malt.

In the present study, a mathematical-statistical approach was applied for modelling and optimization of the composition of malt mixture in order to produce wort with increased biological value and good brewing characteristics.