Sawdust waste as a low-cost support-substrate for laccases production and adsorbent for azo dyes decolorization
Dalel Daâssi1, Hela Zouari-Mechichi2, Fakher Frikha3
1Laboratory of Enzyme Engineering and Microbiology, Ecole Nationale d'Ingénieurs de Sfax, University of Sfax, Route de Soukra Km 4.5, BP 1173, Sfax, 3038 Tunisia ; Department of Biology, Faculty of Sciences and Arts, Khulais, University of Jeddah, Jeddah, Saudi Arabia.
Background:
Laccases are multicopper oxidases with high potential for environmental and industrial applications. Low-cost laccase production could be achieved by solid state fermentation on agro-industrial by-products.
Methods:
A number of agro-industrial solid wastes were tested as support-substrate for laccase production by Coriolopsis gallica under solid-state fermentation (SSF) conditions. Response surface methodology (RSM) was used to optimize the medium composition for laccase production. Initial screening by Plackett-Burman design was performed to select the major variables out of 20 tow medium components fellowing this Central composite design (CCD) was employed to optimize the level of the selected variables.
Results:
Sawdust waste was shown to be the best support-substrate for laccase production by the C. gallica. Peptone as source of organic nitrogen, Cd(2+) as laccase inducer and liquid/solid (L/S) ratio were found to have significant effects on laccase production. Operating at optimum concentrations of the most significant variables (peptone, 4.5 g L(-1), L/S ratio, 5.0 and Cd(+2) 1.0 mM) extracellular laccase activity was enhanced from 1480 U L(-1) (60.5 U g(-1)), to 4880 U L(-1) (200 U g(-1)) which meant a 3.2-fold increase in laccase activity. On the other hand, sawdust waste was studied as a low cost adsorbent to remove the azo dyes Reactive Black 5 (RB5) and Acid Orange 51 (AO51). Decolorization percentages around 67 and 75 % were obtained in 24 h for RB5 and AO51, respectively.
Conclusion:
When used as a support substrate, sawdust yielded the highest laccase production which was increased 3.2 times using RMS optimization.
