Modeling and Optimization of Triticale Wort Production Using an Artificial Neural Network and a Genetic Algorithm
Article (Published version)
Metadata
Show full item recordAbstract
Triticale grain, a wheat–rye hybrid, has been reported to comply very well with the requirements for modern brewing adjuncts. In this study, two triticale varieties, in both unmalted and malted forms, were investigated at various ratios in the grist, applying different mashing regimes and concentrations of the commercial enzyme Shearzyme® 500 L with the aim of evaluating their impact on wort production. In order to capture the complex relationships between the input (triticale ratio, enzyme ratio, mashing regime, and triticale variety) and output variables (wort extract content, wort viscosity, and free amino nitrogen (FAN) content in wort), the study aimed to implement the use of artificial neural networks (ANNs) to model the mashing process. Also, a genetic algorithm (GA) was integrated to minimize a specified multi-objective function, optimizing the mashing process represented by the ANN model. Among the solutions on the Pareto front, one notable set of solutions was found with obje...ctive function values of 0.0949, 0.0131, and 1.6812 for the three conflicting objectives, respectively. These values represent a trade-off that optimally balances the different aspects of the optimization problem. The optimized input variables had values of 23%, 9%, 1, and 3 for the respective input variables of triticale ratio, enzyme ratio, mashing regime, and triticale variety. The results derived from the ANN model, applying the GA-optimized input values, were 8.65% w/w for wort extract content, 1.52 mPa·s for wort viscosity, and 148.32 mg/L for FAN content in wort. Comparatively, the results conducted from the real laboratory mashing were 8.63% w/w for wort extract content, 1.51 mPa·s for wort viscosity, and 148.88 mg/L for FAN content in wort applying same input values. The presented data from the optimization process using the GA and the subsequent experimental verification on the real mashing process have demonstrated the practical applicability of the proposed approach which confirms the potential to enhance the quality and efficiency of triticale wort production.
Keywords:
triticale / adjuncts / mashing / artificial neural networks / genetic algorithmsSource:
Foods - Basel, 2024, 13, 2, 343-Publisher:
- Basel : MDPI
Funding / projects:
- Ministry of Science, Technological Development and Innovation of the Republic of Serbia, institutional funding - 200134 (University of Novi Sad, Faculty of Technology) (RS-MESTD-inst-2020-200134)
- Ministry of Science, Technological Development and Innovation of the Republic of Serbia, institutional funding - 200116 (University of Belgrade, Faculty of Agriculture) (RS-MESTD-inst-2020-200116)
Collections
Institution/Community
FiVeRTY - JOUR AU - Pribić, Milana AU - Kamenko, Ilija AU - Despotović, Saša AU - Mirosavljević, Milan AU - Pejin, Jelena PY - 2024 UR - http://fiver.ifvcns.rs/handle/123456789/4234 AB - Triticale grain, a wheat–rye hybrid, has been reported to comply very well with the requirements for modern brewing adjuncts. In this study, two triticale varieties, in both unmalted and malted forms, were investigated at various ratios in the grist, applying different mashing regimes and concentrations of the commercial enzyme Shearzyme® 500 L with the aim of evaluating their impact on wort production. In order to capture the complex relationships between the input (triticale ratio, enzyme ratio, mashing regime, and triticale variety) and output variables (wort extract content, wort viscosity, and free amino nitrogen (FAN) content in wort), the study aimed to implement the use of artificial neural networks (ANNs) to model the mashing process. Also, a genetic algorithm (GA) was integrated to minimize a specified multi-objective function, optimizing the mashing process represented by the ANN model. Among the solutions on the Pareto front, one notable set of solutions was found with objective function values of 0.0949, 0.0131, and 1.6812 for the three conflicting objectives, respectively. These values represent a trade-off that optimally balances the different aspects of the optimization problem. The optimized input variables had values of 23%, 9%, 1, and 3 for the respective input variables of triticale ratio, enzyme ratio, mashing regime, and triticale variety. The results derived from the ANN model, applying the GA-optimized input values, were 8.65% w/w for wort extract content, 1.52 mPa·s for wort viscosity, and 148.32 mg/L for FAN content in wort. Comparatively, the results conducted from the real laboratory mashing were 8.63% w/w for wort extract content, 1.51 mPa·s for wort viscosity, and 148.88 mg/L for FAN content in wort applying same input values. The presented data from the optimization process using the GA and the subsequent experimental verification on the real mashing process have demonstrated the practical applicability of the proposed approach which confirms the potential to enhance the quality and efficiency of triticale wort production. PB - Basel : MDPI T2 - Foods - Basel T1 - Modeling and Optimization of Triticale Wort Production Using an Artificial Neural Network and a Genetic Algorithm IS - 2 SP - 343 VL - 13 DO - 10.3390/foods13020343 ER -
@article{ author = "Pribić, Milana and Kamenko, Ilija and Despotović, Saša and Mirosavljević, Milan and Pejin, Jelena", year = "2024", abstract = "Triticale grain, a wheat–rye hybrid, has been reported to comply very well with the requirements for modern brewing adjuncts. In this study, two triticale varieties, in both unmalted and malted forms, were investigated at various ratios in the grist, applying different mashing regimes and concentrations of the commercial enzyme Shearzyme® 500 L with the aim of evaluating their impact on wort production. In order to capture the complex relationships between the input (triticale ratio, enzyme ratio, mashing regime, and triticale variety) and output variables (wort extract content, wort viscosity, and free amino nitrogen (FAN) content in wort), the study aimed to implement the use of artificial neural networks (ANNs) to model the mashing process. Also, a genetic algorithm (GA) was integrated to minimize a specified multi-objective function, optimizing the mashing process represented by the ANN model. Among the solutions on the Pareto front, one notable set of solutions was found with objective function values of 0.0949, 0.0131, and 1.6812 for the three conflicting objectives, respectively. These values represent a trade-off that optimally balances the different aspects of the optimization problem. The optimized input variables had values of 23%, 9%, 1, and 3 for the respective input variables of triticale ratio, enzyme ratio, mashing regime, and triticale variety. The results derived from the ANN model, applying the GA-optimized input values, were 8.65% w/w for wort extract content, 1.52 mPa·s for wort viscosity, and 148.32 mg/L for FAN content in wort. Comparatively, the results conducted from the real laboratory mashing were 8.63% w/w for wort extract content, 1.51 mPa·s for wort viscosity, and 148.88 mg/L for FAN content in wort applying same input values. The presented data from the optimization process using the GA and the subsequent experimental verification on the real mashing process have demonstrated the practical applicability of the proposed approach which confirms the potential to enhance the quality and efficiency of triticale wort production.", publisher = "Basel : MDPI", journal = "Foods - Basel", title = "Modeling and Optimization of Triticale Wort Production Using an Artificial Neural Network and a Genetic Algorithm", number = "2", pages = "343", volume = "13", doi = "10.3390/foods13020343" }
Pribić, M., Kamenko, I., Despotović, S., Mirosavljević, M.,& Pejin, J.. (2024). Modeling and Optimization of Triticale Wort Production Using an Artificial Neural Network and a Genetic Algorithm. in Foods - Basel Basel : MDPI., 13(2), 343. https://doi.org/10.3390/foods13020343
Pribić M, Kamenko I, Despotović S, Mirosavljević M, Pejin J. Modeling and Optimization of Triticale Wort Production Using an Artificial Neural Network and a Genetic Algorithm. in Foods - Basel. 2024;13(2):343. doi:10.3390/foods13020343 .
Pribić, Milana, Kamenko, Ilija, Despotović, Saša, Mirosavljević, Milan, Pejin, Jelena, "Modeling and Optimization of Triticale Wort Production Using an Artificial Neural Network and a Genetic Algorithm" in Foods - Basel, 13, no. 2 (2024):343, https://doi.org/10.3390/foods13020343 . .