Statistical optimization of culture conditions for bacterial cellulose production using Box‐Behnken design

Abstract

Culture conditions in a jar fermentor for bacterial cellulose (BC) production from A. xylinum BPR2001 were optimized by statistical analysis using Box-Behnken design. Response surface methodology was used to predict the levels of the factors, fructose (X1), corn steep liquor (CSL) (X2), dissolved oxygen (DO) (X3), and agar concentration (X4). Total 27 experimental runs by combination of each factor were carried out in a 10-L jar fermentor, and a three-dimensional response surface was generated to determine the effect of the factors and to find out the optimum concentration of each factor for maximum BC production and BC yield. The fructose and agar concentration highly influenced the BC production and BC yield. However, the optimum conditions according to changes in CSL and DO concentrations were predicted at almost central values of tested ranges. The predicted results showed that BC production was 14.3 g/L under the condition of 4.99% fructose, 2.85% CSL, 28.33% DO, and 0.38% agar concentration. On the other hand, BC yield was predicted in 0.34 g/g under the condition of 3.63% fructose, 2.90% CSL, 31.14% DO, and 0.42% agar concentration. Under optimized culture conditions, improvement of BC production and BC yield were experimentally confirmed, which increased 76% and 57%, respectively, compared to BC production and BC yield before optimizing the culture conditions.

Statistical culture-based strategies to enhance chlamydospore production by Trichoderma harzianum SH2303 in liquid fermentation

“…The Box-Behnken design (BBD) is a statistical method that is used to study the maximum response corresponding to the best interactions in a multifactor system (Bae and Shoda, 2005). Each factor, or independent variable, is placed at one of three equal spaced values, usually coded as −1, 0, +1.…”

Process Parameters for Fermentation in a Rotary Disc Reactor for Optimum Microbial Cellulose Production using Response Surface Methodology

“…Firstly, the fermentation composition, including the carbon, mineral, and nitrogen sources used in the medium (Keshk and Sameshima 2005). Traditional sources of carbon for microbial fermentation are sugars such as glucose, fructose, and sucrose (Bae and Shoda 2005). More recently, unconventional feedstocks from renewable resources and waste streams have been investigated (Zeng et al 2011).…”

Statistical Optimization of Medium Components by Response Surface Methodology for Enhanced Production of Bacterial Cellulose by Gluconacetobacter persimmonis

“…al., [6] brought about improvement in BC yield using poor quality horticulture substrates using Taguchi method. In the study carried-out by Bae and Shoda [3], culture conditions in a jar fermenter for BC production were optimized using Box-Behnken design, Response surface methodology was used to predict the levels of various factors.…”

“…Hence it becomes necessary to optimize the yields of cellulose production by the use of process improvement strategies. Various workers have optimized medium constituents and process parameters for increased bacterial cellulose (BC) yield [2][3][4][5][6].…”

“…On the other hand, statistical experimental designs provide a systematic and efficient plan for experimentation to achieve certain goals so that many factors can be simultaneously studied. Therefore, in recent years number of statistical designs were used to search the key factors rapidly from a multivariable system, such as Plackett-Burman design and response surface methodology [2,3,5,[7][8][9] because statistical optimization not only allows quick screening of large experimental domain, but also reflects the role of each of the components and their interactions.…”