Comparative Evaluation of Artificial Neural Networks and Response Surface Methodology for Nitrogen Source Optimization in Xylitol Production

Xylitol is a five-carbon sugar alcohol of industrial interest due to its applications as a food sweetener and sugar substitute. In this study, artificial neural networks combined with a genetic algorithm were evaluated as a data-driven approach for modeling and exploring xylitol production by Spathaspora boniae and Spathaspora brasiliensis during fermentation of sugarcane bagasse hemicellulosic hydrolysate. The dataset comprised 20 experimental points obtained from a face-centered central composite design, using urea, yeast extract, peptone, and ammonium sulfate as input variables. The neural network models showed high goodness-of-fit, with R2 values of 0.9952 for S. boniae and 0.9930 for S. brasiliensis. Experimental validation of the optimized conditions resulted in xylitol production of 11.54 ± 0.52 g L−1 for S. boniae and 9.29 ± 0.24 g L−1 for S. brasiliensis. Comparison with response surface methodology showed that both approaches provided strong predictive performance, although the statistical model predicted the optimum conditions more accurately. For S. boniae, however, the ANN-GA approach identified an alternative condition associated with lower nitrogen supplementation and higher experimental xylitol production. Given the limited dataset, this study should be regarded as a proof-of-concept for the application of data-driven optimization tools to xylitol fermentation. The results indicate that ANN-GA can complement classical statistical methods by helping to identify alternative operating conditions in bioprocess optimization.