Al-Shorgani, NKNNKNAl-ShorganiIsa, MHMMHMIsaYusoff, WMWWMWYusoffKalil, MSMSKalilHamid, AAAAHamid2024-05-292024-05-2920151543-50831543-507510.1080/15435075.2014.895738WOS:0003578637000062-s2.0-84937044707https://www.scopus.com/inward/record.uri?eid=2-s2.0-84937044707&doi=10.1080%2f15435075.2014.895738&partnerID=40&md5=8462fe5687f09e25069c4ec59b0c8ebehttps://oarep.usim.edu.my/handle/123456789/11974In this study, response surface methodology (RSM) was applied to optimize and investigate the ability of yeast extract, CaCO3, MgSO4, and K2HPO4 to maximize biobutanol production by a novel local isolate of Clostridium acetobutylicum YM1. A central composite design was applied as the experimental design, and analysis of variance (ANOVA) was used to analyze the experimental data. A quadratic polynomial equation was obtained for biobutanol production by multiple regression analysis. ANOVA analysis showed that the model was significant (p < 0.0001), and the yeast extract, CaCO3, and MgSO4 concentrations had a significant effect on biobutanol production. However, K2HPO4 did not have a significant effect on biobutanol production. The estimated optimum combinations for biobutanol production using C. acetobutylicum YM1 were 2 g/L yeast extract, 6 g/L CaCO3, 0.1 g/L MgSO4, and 1.1 g/L K2HPO4. Subsequently, the model was validated through use of the estimated optimum conditions, which confirmed the model validity and 13.67 g/L of biobutanol was produced.en-USBiobutanolClostridium acetobutylicum YM1Medium optimizationResponse surface methodologyRenewable energyArticle123612431212