Details

Autor(en) / Beteiligte

• Hutchens, S.A
• León, R.V
• O'Neill, H.M
• Evans, B.R

Titel

Statistical analysis of optimal culture conditions for Gluconacetobacter hansenii cellulose production

Ist Teil von

• Letters in applied microbiology, 2007-02, Vol.44 (2), p.175-180

Ort / Verlag

Oxford, UK: Oxford, UK : Blackwell Publishing Ltd

Erscheinungsjahr

2007

Quelle

Access via Wiley Online Library

Beschreibungen/Notizen

• The purpose of this study was to analyse the effects of different culture parameters on Gluconacetobacter hansenii (ATCC 10821) to determine which conditions provided optimum cellulose growth. Five culture factors were investigated: carbon source, addition of ethanol, inoculation ratio, pH and temperature. [smallcapital jmp] Software (SAS, Cary, NC, USA) was used to design this experiment using a fractional factorial design. After 22 days of static culture, the cellulose produced by the bacteria was harvested, purified and dried to compare the cellulose yields. The results were analysed by fitting the data to a first-order model with two-factor interactions. The study confirmed that carbon source, addition of ethanol, and temperature were significant factors in the production of cellulose of this G. hansenii strain. While pH alone does not significantly affect average cellulose production, cellulose yields are affected by pH interaction with the carbon source. Culturing the bacteria on glucose at pH 6·5 produces more cellulose than at pH 5·5, while using mannitol at pH 5·5 produces more cellulose than at pH 6·5. The bacteria produced the most cellulose when cultured on mannitol, at pH 5·5, without ethanol, at 20°C. Inoculation ratio was not found to be a significant factor or involved in any significant two-factor interaction. These findings give insight into the conditions necessary to maximize cellulose production from this G. hansenii strain. In addition, this work demonstrates how the fractional factorial design can be used to test a large number of factors using an abbreviated set of experiments. Fitting a statistical model determined the significant factors as well as the significant two-factor interactions.