Effect of operating parameters on mechanical expression of solvent-soaked soybean-grits.

Oil from soybean is obtained mostly by solvent extraction of soybean flakes. Legislation banning the use of hexane as solvent for extracting edible vegetable oil has forced a search for an alternative solvent and for developing a suitable oil recovery process. Expellers are being used for obtaining vegetable oil by mechanical means (expression) from oil seeds having oil content higher than 20 %. It was felt, in view of the stiffness of the soybean matrix, a combination of solvent treatment and expression could be a cheaper alternative; thus an attempt has been made here to develop a two stage process constituting soaking of soybean grits in solvent followed by mechanical compression (hydraulic press) of solvent-soaked grits to recover oil. The present work aimed at studying the effect of various process parameters on oil yield from solvent soaked soybean-grits during soaking as well as pressing stages using the solvents: hexane, ethanol (alternative solvent). The process parameters were identified through holistic approach. The dependant variable was oil recovery (expressed as fraction of initial oil content of soybean) whereas the independent parameters were particle size, solvent-bean mass ratio, soaking time, soaking temperature, applied pressure and pressing time. The effect of each of the above parameters on fractional oil recovery (FOR) was studied. The results of the present study indicate that the above parameters have a significant effect on the fractional oil recovery with particle size, soaking temperature, soaking time and pressing time being the most significant factors. The present study also indicates that ethanol can be used as an alternate solvent to hexane by optimizing the factors as discussed in this paper.

Growth Characteristics Modeling of Mixed Culture of Bifidobacterium bifidum and Lactobacillus acidophilus using Response Surface Methodology and Artificial Neural Network

Different culture conditions viz. additional carbon and nitrogen content, inoculum size and age, temperature and pH of the mixed culture of Bifidobacterium bifidum and Lactobacillus acidophilus were optimized using response surface methodology (RSM) and artificial neural network (ANN). Kinetic growth models were fitted for the cultivations using a Fractional Factorial (FF) design experiments for different variables. This novel concept of combining the optimization and modeling presented different optimal conditions for the mixture of B. bifidum and L. acidophilus growth from their one variable at-a-time (OVAT) optimization study. Through these statistical tools, the product yield (cell mass) of the mixture of B. bifidum and L. acidophilus was increased. Regression coefficients (R2) of both the statistical tools predicted that ANN was better than RSM and the regression equation was solved with the help of genetic algorithms (GA). The normalized percentage mean squared error obtained from the ANN and RSM models were 0.08 and 0.3%, respectively. The optimum conditions for the maximum biomass yield were at temperature 38°C, pH 6.5, inoculum volume 1.60 mL, inoculum age 30 h, carbon content 42.31% (w/v), and nitrogen content 14.20% (w/v). The results demonstrated a higher prediction accuracy of ANN compared to RSM.

Growth characteristics modeling of Lactobacillus acidophilus using RSM and ANN

The culture conditions viz. additional carbon and nitrogen content, inoculum size, age, temperature and pH of Lactobacillus acidophilus were optimized using response surface methodology (RSM) and artificial neural network (ANN). Kinetic growth models were fitted to cultivations from a Box-Behnken Design (BBD) design experiments for different variables. This concept of combining the optimization and modeling presented different optimal conditions for L. acidophilus growth from their original optimization study. Through these statistical tools, the product yield (cell mass) of L. acidophilus was increased. Regression coefficients (R²) of both the statistical tools predicted that ANN was better than RSM and the regression equation was solved with the help of genetic algorithms (GA). The normalized percentage mean squared error obtained from the ANN and RSM models were 0.06 and 0.2%, respectively. The results demonstrated a higher prediction accuracy of ANN compared to RSM.