Statistical Optimization of Fermentation Conditions for Cellulase Production from Palm Oil Mill Effluent
Jamal I. Daoud and Md. Zahangir Alam
DOI : 10.3844/ajessp.2010.66.70
American Journal of Environmental Sciences
Volume 6, Issue 1
Problem statement: Palm oil mill effluent discharged by the oil palm industries is considered as the mixed of high polluted effluent which is abundant (about 20 million tonnes year-1) and its effect contributes to the serious environmental problems through the pollution of water bodies. Approach: The aim of this study was to identify the potential of low cost substrate such as Palm Oil Mill Effluent (POME) for the production of cellulase enzyme by liquid state bioconversion. The filamentous fungus Trichoderma harzianum was used for liquid state bioconversion of POME for cellulase production. Statistical optimization was carried out to evaluate the physico-chemical parameters (factors) for maximum cellulase production by 2-level fractional factorial design with six central points. The polynomial regression model was developed using the experimental data including the effects of linear, quadratic and interaction of the factors. The factors involved were substrate (POME) and co-substrate (wheat flour) concentrations, temperature, pH, inoculum and agitation. Results: Statistical analysis showed that the optimum conditions were: Temperature of 30°C, substrate concentration of 2%, wheat flour concentration of 3%, pH of 4, inoculum of 3% and agitation of 200 rpm. Under these conditions, the model predicted the enzyme production to be about 14 FPU mL-1. Analysis Of Variance (ANOVA) of the design showed a high coefficient of determination (R2) value of 0.999, thus ensuring a high satisfactory adjustment of the quadratic model with the experimental data. Conclusion/Recommendations: This study indicates a better solution for waste management through the utilization of POME for cellulase production that could be used in the industrial applications such as bioethanol production.
© 2010 Jamal I. Daoud and Md. Zahangir Alam. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.