Statistical Optimization in the Fermentation Stage for Organic Ethanol: A Sustainable Approach

The growing demand for organic products is having a transformative effect on the alcoholic beverage industry. This work investigates the possibility of producing organic ethanol only from sugarcane final molasses as a nutrient vector and Saccharomyces cerevisiae in the absence of inorganic nitrogen or phosphorus compounds. The Plackett–Bürman design included the pseudo-factors (X4–X6) due to the experimental design requirements. These factors represent the possible influence of uncontrolled variables, such as pH or nutrient interactions. Subsequently, a predictive quadratic model using Box–Behnken design with the real variables (sugar concentration, yeast dose, and incubation time) was developed and validated (R2=0.977) with internal validation; given the lack of replications and the sample size, this value should be interpreted with caution and not as generalizable predictive evidence. Further experiments with replications and cross-validation will be required to confirm its predictive capacity. Through statistical optimization, the maximum cell proliferation of 432×106 cells/mL was achieved under optimal conditions of 8°Brix sugar concentration, 20 g/L dry yeast, and 3 h incubation time. The optimized fermentation process produced 7.8% v/v ethanol with a theoretical fermentation efficiency of 78.52%, an alcohol-to-substrate yield of 62.15%, and a productivity of 1.86 g/L·h, representing significant improvements of 21.9%, 24.6%, 31.0%, and 10.1%, respectively, compared with non-optimized conditions. The fermentation time was reduced from 48 to 42 h while maintaining superior performance. These results demonstrate the technical feasibility of producing organic ethanol using certified organic molasses and no chemical additives. Overall, these findings should be regarded as proof of concept. All experiments were single-run without biological or technical replicates; consequently, the optimization and models are preliminary and require confirmation with replicated experiments and external validation.