MONITORING OF BIOSURFACTANT PRODUCTION BY Bacillus subtilis USING BEET PEEL AS CULTURE MEDIUM VIA THE DEVELOPMENT OF A NEURAL SOFT-SENSOR IN AN ELECTRONIC SPREADSHEET

Thus, a central composite rotatable design (CCRD) and responses were evaluated by response surface methodology (RSM) modeling. The optimal operation values determined were 200 rpm (agitation) and 0.5 vvm (aeration), reaching values of 1931.2 mg/L of crude biosurfactant concentration and 28.37 mN/m of surface tension.

For the development of a mathematical model based on an artificial neural network (ANN), the experimental data from each run (CCRD) of the bioreactor were used. The results indicated a topology of 6-6-1 neurons with an excellent predictive capacity of biosurfactant concentration: dispersion plot with R2 of 0.995, and error criteria SSE of 0.31, MSE of 7.29×10-4 and RSME of 2.7×10-2. A soft sensor was then designed in an electronic spreadsheet, computing the biosurfactant production from secondary measurements.