The beer quality can be modulated from changes in their ingredient proportions, as well as in operating parameters. The crossed experimental designs and the multiple optimizations based on desirability functions have demonstrated to be effective methodologies in the unit operation polynomial modeling and optimization of bioprocess, respectively. However, artificial intelligence techniques have been used as an alternative to this modeling in bioprocess. Therefore, this study aimed to implement a software combining artificial neural network (ANN) and differential evolution to optimize the topology of an ANN to model the Ale beer production and to use the optimized ANN in ingredients and operation parameters choice that ensure a beer with high acceptance rate, by the genetic algorithm technique for multiple-objective function. This approach allowed to find ANN models which fitted the process with correlation coefficients higher than 0.85 and high satisfaction level of beer desirable quality attributes (global desirability value = 0.78). Practical ApplicationsThis manuscript could be useful for bioprocess professionals involved in the development of the brewing process and artificial intelligence applications. The approach applied in this work allows for modeling and optimization of brewing process using a combination of crossed experimental design, artificial neural networks, and evolutionary algorithms with relatively low experimental efforts. At the same time, the quality attributes of the beer are better controlled.

This work aimed to determinate eight beer properties using UV-Vis spectra in combination with principal component regression (PCR) or artificial neural network (ANN) models. A statistical experimental design was performed to generate the calibration data. First, principal component analysis (PCA) was applied to the original spectral data, and the scores in significant PCs were utilized to calibrate both models. PCR showed poor correlation for beer parameters (R 2 < 0.61). The ANNs showed satisfactory correlations (R 2 = 0.74-0.92) and low relative error considering a variable range (E r < 9%) for most of the beer-quality attributes, but vicinal diketones (R 2 = 0.56, E r = 16.69%). Once implemented, this method would be fast and low cost. ARTICLE HISTORY