Statistical optimization of fermentable sugar extraction from the Malaysian brown alga Sargassum binderi

Statistical optimisation techniques were applied in the present study to extract fermentable sugar (FS) from indigenous brown seaweed, Sargassum binderi. As identified by the Plackett-Burman design (PBD) with five variables, the most significant parameters (p < 0.05) affecting sugar yield were pretreatment temperature, concentration of sulphuric acid (H2SO4) used during pretreatment, and loading of enzyme cellulase during hydrolysis process. Upon this, the extraction of FS from S. binderi was further optimized with central composite design (CCD) by using response surface methodology (RSM). Analysis of variance revealed that every independent variable in the present study possesses significant positive effect towards the yield of FS. In addition, interactions among the independent variables were observed as well. The model proposed in this study fits significantly well to the experimental data with more than 95 % confidence. The optimal condition as proposed by the cubic model for maximum yield of FS was found as follows: pretreatment of S. binderi with 6.38 % of dilute H2SO4 at 120.7 A degrees C, followed by hydrolysis using 0.16 mL of cellulase loading per unit gram of pretreated dry seaweed. The results of validation experiments gave FS yield of 1.69 g g(-1) which fitted well with predicted value by RSM (1.72 g g(-1)). The overall error was small indicating the proficiency of the models in optimizing extraction of FS from S. binderi. The present study indicated that the brown seaweed S. binderi has potential to become a dependable biomass source for production of value added products.