Asian Journal of Microbiology, Biotechnology & Environmental Sciences Paper

Vol 205, Issue 4, 2018; Page No.(1294-1305)

PRODUCTION AND OPTIMIZATION OF POLYHYDROXY BUTYRATE (PHB) FROM AZOTOBACTER CHROOCOCCUM (A3) STRAIN FOR SCALE UP USING CENTRAL COMPOSITE DESIGN

R. RAM NARENDRAN, S.F. MALEEKA BEGUM AND G. AYYAPPADASAN

Abstract

Two Poly-β-hydroxybutyrate (PHB) producing strains of Azotobacter chroococcum (A3 and A4 strains) were isolated from waste soils and the mass production was optimized using various parameters such as pH, temperature, carbon sources, nitrogen sources, etc. The optimized conditions were used for the production of PHB and large scale production was carried out in fed batch fermentor in order to facilitate industrial production. The optimum temperature for PHB production was found to be 37°C for A3 and 33°C for A4 strains respectively. The optimum pH for the PHB production was 7. The fructose yielded the highest production of PHB as the best carbon source. Similarly, ammonium chloride yielded the highest production which registered as 5.8 % as the best nitrogen source. The purified PHB was quantified and estimated in g/L which was confirmed by UV Spectroscopy in which the presence of peak obtained was between 230 – 240 nm. Scanning Electron Microscope analysis showed amorphous porous molecules produced by the bacteria A. chroococcum and the functional group was determined by Fourier-transform infrared spectroscopy (FTIR) method which represents the presence of –C–O–C and C=O. Molecular analysis of PHB producing genes were amplified and confirmed the presence of PHB producing genes of the strain A. chroococcum A3. Production optimization of PHB was investigated using Central Composite Design using four different factors viz., pH, temperature, RPM, and time period and statistically validated. The one and two variable factor revealed significance with p-value less than 0.1 and 1 respectively. The lack of fit has a p-value of 0.26 which is not significant. ANN analytical tool was used for the optimization of mass production of PHB. The results were elucidated that the training, validation, test and overall performance of network based on iteration was validated using ANN. The correlation co-efficient revealed more than R = 0.9 for given proposed design model for the PHB production.

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