TITLE:
Optimization Techniques and Development of Neural Models Applied in Biosurfactant Production by Bacillus subtilis Using Alternative Substrates
AUTHORS:
Juliana Ferrari Ferreira Secato, Brunno Ferreira dos Santos, Alexandre Nunes Ponezi, Elias Basile Tambourgi
KEYWORDS:
Biosurfactant, Bacillus subtilis, Response Surface Methodology, Artificial Neural Network, Oil Spreading, Waste Management
JOURNAL NAME:
Advances in Bioscience and Biotechnology,
Vol.8 No.10,
October
13,
2017
ABSTRACT: Bacillus subtilis was investigated as production of biosurfactant using a combination
based on waste of candy industry and glycerol from biodiesel production process
as only substrate. The experimental design chosen for optimization by response
surface methodology was a central composite rotatable design (CCRD) and dry
weight (DW) and crude biosurfactant (CB) concentrations were selected as
responses in analysis. Two techniques were implemented response surface
methodology (RSM) and artificial neural network (ANN). First challenge of study
was to assess the effects of the interactions between variables and reach
optimum values. With the CCRD results, RSM and ANN models were developed,
optimizing the production of biosurfactant. The correlation coefficients (R2)
of RSM models explained 88% for DW and 73% for CB of the interactions among
substrate concentrations, while ANN models explained 99% for DW and 98% for CB,
demonstrating that developed ANN models were more accurate and consistent in
predicting optimized conditions than RSM model. The maximum DW and CB produced
in the optimum conditions were 25.60 ± 5.0 g/L and 668 ± 40 mg/L, respectively.
The crude biosurfactant also showed applications in cases of oil spreading in
water due to clear zone produced in Petri dishes assays.