期刊
BIORESOURCE TECHNOLOGY
卷 101, 期 7, 页码 2367-2374出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.biortech.2009.11.056
关键词
Docosahexaenoic acid; Aurantiochytrium; Two-stage fermentation; Statistical designs; Artificial neural networks
Statistical screening experimental designs were applied to identify the significant culture variables for biomass production of Aurantiochytrium limacinum SR21 and their optimal levels were found using a combination of Artificial Neural Networks, genetic algorithms and graphical analysis. The biomass value obtained (40.3 g cell dry weight l(-1)) employing the selected culture conditions agreed with that predicted by the model. Subsequently, two significant culture conditions for docosahexaenoic acid (DHA) production were determined, finding that an inoculum of 10% (v/v), obtained from the previous (statistically optimized) stage, should be used in a DHA production medium having a molar C:N ratio of 55: 1, to reach a production of 7.8 g DHA l(-1) d(-1). The production step was thereafter scaled in a 3.5 1 bioreactor, and DHA productivity of 3.7 g l(-1) d(-1) was obtained. This two-stage strategy: statistically optimized inoculum production (fist step) and a DHA production step, is presented for the first time to optimize a bioprocess conducive to the obtention of microbial DHA. (C) 2009 Elsevier Ltd. All rights reserved.
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