Optimization Study by Response Surface Methodology and Artificial Neural Network on the Culture Parameters of Citric Acid Bioproduction from Sweet Potato Peels
Keywords:
Optimization techniques, production, chemical processing, methodology, artificial neural network, biomass, yeast formation, bioproduction techniquesAbstract
In the cause of this research work, two steps enzymatic hydrolysis of sweet potato peel was carried out respectively. The effect of α-amylase dose, reaction time, and reaction temperature and biomass weight on glucose concentration was investigated carefully. The response surface methodology (REM) predicted the highest reducing sugar concentration at liquefaction to be 74 g/L, at the following optimized conditions; temperature of 45°C, α-amylase dose 0.7 %v/v, biomass weight 22.345 g and time of 15 minutes, while the laboratory experimental yield was 73.5 g/L which expressly after collection of results shows that the result is good and adoptable for this purpose. An electro thermal oven with the model DHG and temperature control and timer were used in a dry cell weight (DCW) analysis of the sweet potatoes peels to estimate the dry weight of the samples collected and analyzed. Eco tester pH2 was also used which was calibrated using buffer solutions. Weighing balance was also used during the experiment precisely Mettler Toledo model FA 2104 which was able to weigh up to 0.02 g to 210 g. Timing was also done by regulating the timing button and centrifuge was used to spin the sample in order to separate undissolved solution from solvent. A comparison graph was plotted to ascertain the differentials of citric and yeast against time and another comparison for citric and yeast against biomass. In all, the analysis proved that all the apparatus, methods, parameters, and investigatory measures adopted in this research work was useful and 98% reliable for citric acid bioproduction using sweet potato.
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