A modeling method for the development of a bioprocess to optimally produce umqombothi (a South African traditional beer)

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.

Physicochemical and microbiological changes during two-stage fermentation production of umqombothi

“…Sample U1 had a significantly ( p < 5.99) higher TSS than U3 and U2 respectively. Cooking the soured porridge for a suitable period is required for starch gelatinization and the release of nutrients bound up in yeast cells [ 16 ]. The cooking duration was discovered to influence the alcohol concentration, TSS, and pH.…”

“…A sensory evaluation was conducted on the final product samples for each batch. The samples were evaluated for physicochemical, chemical, microbiological and sensory analysis [ 16 ].…”

Optimization of preparation conditions for Salsola laricifolia protoplasts using response surface methodology and artificial neural network modeling

“…These metrics included the correlation coefficient (R), the coefficient of determination (R 2 ), the root mean square error (RMSE), and the mean absolute percentage error (MAPE). A higher value of R and R 2 indicates a stronger correlation and better fitting ability of the model [ 50 , 51 ]. Smaller values of RMSE and MAPE indicate a lower deviation and relative error between the predicted and actual values, indicating the higher accuracy in the model’s predictions [ 33 , 52 ].…”

Experimental and Statistical Study on the Properties of Basic Oxygen Furnace Slag and Ground Granulated Blast Furnace Slag Based Alkali-Activated Mortar

“…In this study, the commercially available Design-Expert ® 11 software was implemented to perform statistical interpretation, mathematical modeling, and the optimization of the mix designs using RSM. RSM is considered an efficacious statistical tool that is mainly employed for experimental design, mathematical modeling, and optimization [ 43 ]. RSM assists in the evaluation of responses that are affected by one or more factors [ 44 ].…”