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Performance of Artificial Neural Network for Predicting Fermentation Characteristics in Biosurfactant Production by Bacillus subtilis ATCC 6633 using Sugar Cane Molasses
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Yousef Rahimi Kashkouli
Veröffentlicht/Copyright:
13. Dezember 2011
Artificial neural network (ANN) was successfully applied to model fermentation parameters for biosurfactant production by Bacillus subtilis ATCC 6633 using sugar cane molasses. Cell growth and biosurfactant production were monitored along the surface activity of the cell-free broth. Response surface methodology (RSM) as a formal statistical model building system was used for the ANN development. The network predicted biosurfactant concentration was 0.381 g/l which showed almost no differences with the relevant experimental value which obtained according to the RSM arrangement. Furthermore, the ANN surface tension reduction was 30.48 mN/m, which was within 3.24% of the experimental value. Comparisons between RSM and the ANN showed preference of using ANN as complementary to RSM and not as a replacement to it.
Keywords: artificial neural network; sugar cane molasses; biosurfactant; surface tension reduction; Bacillus subtilis; response surface methodology
Published Online: 2011-12-13
©2012 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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Schlagwörter für diesen Artikel
artificial neural network;
sugar cane molasses;
biosurfactant;
surface tension reduction;
Bacillus subtilis;
response surface methodology
Artikel in diesem Heft
- Article
- Production of Gallic Acid by Immobilized Aspergillus niger Using Polyurethane Foam as Solid Support
- Comparison of Vacuum Cooling with Conventional Cooling for Purslane
- Adsorption Isotherms for Red Onion Slices Using Empirical and Neural Network Models
- Low Temperature Drying With Air Dehumidified by Zeolite for Food Products: Energy Efficiency Aspect Analysis
- Performance of Artificial Neural Network for Predicting Fermentation Characteristics in Biosurfactant Production by Bacillus subtilis ATCC 6633 using Sugar Cane Molasses
- Optimized Neural Network for Instant Coffee Classification through an Electronic Nose
- Optimization of Extraction of D-pinitol and Phenolics from Cultivated and Wild Types of Carob Pods Using Response Surface Methodology
- Mechanical Damage to Pinto Bean Seeds as Affected by Moisture Content, Impact Velocity and Seed Orientation
- Gel Properties of Ribbonfish (Trichiurus haumela) Surimi Gels with Soybean Dietary Fiber Induced by High Pressure and Heating
- Correlating the Data on the Mechanical Damage to Mung Bean Seeds under Impact Loading
- Numerical Simulation of Experimental Freezing Process of Ground Meat Cylinders
- Mathematical Modelling of the Heat Transfer and Microbial Inactivation During a Meat Pet Food Sterilization in Retortable Pouches
- Evaluation of Boundary Conditions for CFD Simulation of Liquid Food Thermal Process in Glass Bottles
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