Abstract
Bioprocess development for umqombothi (a South African traditional beer) as with other traditional beer products can be complex. As a result, beverage bioprocess development is shifting towards new systematic protocols of experimentation. Traditional optimization methods such as response surface methodology (RSM) require further comparison with a relevant machine learning system. Artificial neural network (ANN) is an effective non-linear multivariate tool in bioprocessing, with enormous generalization, prediction, and validation capabilities. ANN bioprocess development and optimization of umqombothi were done using RSM and ANN. The optimum condition values were 1.1 h, 29.3 °C, and 25.9 h for cooking time, fermentation temperature, and fermentation time, respectively. RSM was an effective tool for the optimization of umqombothi’s bioprocessing parameters shown by the coefficient of determination (R2) closer to 1. RSM significant parameters: alcohol content, total soluble solids (TSS), and pH had R2 values of 0.94, 0.93, and 0.99 respectively while the constructed ANN significant parameters: alcohol content, TSS, and viscosity had R2 values of 0.96, 0.96, and 0.92 respectively. The correlation between experimental and predicted values suggested that both RSM and ANN were suitable bioprocess development and optimization tools.
Highlights
Bioprocess development for umqombothi as with other traditional beer products can be complex
Optimization of cooking time, fermentation temperature, and fermentation time is essential for maintaining consistent physicochemical properties, curbing undesired changes that may occur during bioprocessing, and understanding the interactions among these process variables at different
Samples fermented for a longer period (≥ 60 h) at a relatively higher temperature (≥ 30 °C) contained a lower alcohol content (Table 3, see experimental run numbers 1, 4, 7, 9, 15, and 20)
Summary
Bioprocess development for umqombothi (a South African traditional beer) as with other traditional beer products can be complex. Beverage bioprocess development is shifting towards new systematic protocols of experimentation. Traditional optimization methods such as response surface methodology (RSM) require further comparison with a relevant machine learning system. Artificial neural network (ANN) is an effective non-linear multivariate tool in bioprocessing, with enormous generalization, prediction, and validation capabilities. RSM was an effective tool for the optimization of umqombothi’s bioprocessing parameters shown by the coefficient of determination (R2) closer to 1. The correlation between experimental and predicted values suggested that both RSM and ANN were suitable bioprocess development and optimization tools. The combination of linear and non-linear techniques is an effective approach to describe, analyze, and predict bioprocess responses that impact the outcomes of the final p
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