A comparison between the classic Plackett-Burman design (PB) ANOVA analysis and a genetic algorithm (GA) approach to identify significant factors have been carried out. This comparison was made by applying both analyses to data obtained from the experimental results when optimizing both chemical and enzymatic hydrolysis of three lignocellulosic feedstocks (corn and wheat bran, and pine sawdust) by a PB experimental design. Depending on the kind of biomass and the hydrolysis being considered, different results were obtained. Interestingly, some interactions were found to be significant by the GA approach and allowed to identify significant factors, that otherwise, based only in the classic PB analysis, would have not been taken into account in a further optimization step. Improvements in the fitting of ca. 80% were obtained when comparing the coefficient of determination (R(2)) computed for both methods. (C) 2011 Elsevier Ltd. All rights reserved.