Statistical optimization of the extraction of citric acid from the solid fermented substrate of empty fruit bunches. Issue 8 (19th July 2020)

• Record Type: Journal Article
• Authors: Bari, Md Niamul; Alam, Md Zahangir; Mamun, Abdullah‐Al; Khatun, Hanufa
• Is Part Of: Engineering reports. Volume 2:Issue 8(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-07-19
• Subjects: A niger -- citric acid -- extraction -- oil palm empty fruit bunches (EFB) -- optimization -- solid state bioconversion; Engineering -- Periodicals; Computer science -- Periodicals; 620.005
• DOI: 10.1002/eng2.12186
• Languages: English
• ISSNs: 2577-8196

Abstract

Abstract: Optimization of the process parameters for the extraction of a product from a solid substrate after bioconversion is essentially important to maximize the yield. The extraction of citric acid from the solid substrate of oil palm empty fruit bunches (EFB) after bioconversion was initially optimized by following single factor variation. Following this, the extraction parameters were optimized statistically with the help of experimental design by Box‐Behnken Design under Response Surface Methodology through the development of a second order regression model. The statistical analysis of the result showed that in the range studied all three factors, that is, shaking speed, solvent ratio, and shaking time, had a significant influence on the citric acid extraction. The highest amount of citric acid extracted was 337.34 ± 1.1 g/kg‐dry EFB, for which the extraction parameters were a shaking speed of 125 rpm, a shaking time of 58.5 minutes, and a solvent ratio of 10.70. The coefficient of determination observed ( R 2 ) from the analysis was .9921, indicating a satisfactory fit of the model with the response. The analysis showed that all the terms of the model were highly significant with the P‐ value <.05. Abstract : The process parameters of citric acid extraction from solid substrate after bioconversion are statistically optimized. The developed optimization model proves to be highly significant to determine the parameters within the experimental limits. The model prediction is also experimentally validated.