G.C. Majumdar

Optimization of Process Parameters for Water Extraction of Stevioside using Response Surface Methodology

Hot water extraction process was used for the extraction of steviosides from dry stevia leaves. The independent variables were, leaf to water ratio (1:5 to 1:20), heating time (10 to 120 min), and temperature (30 to 90°C). The combined effects of these independent variables on the extracted stevioside concentration and color of the extract were studied. For optimizing the extraction process, central composite rotatable design in combination with response surface methodology was used. Significant regression models with coefficient of determination greater than 0.90 were established to study the effect of independent variables on the responses. The optimum conditions are: temperature of water: 78°C, time of heating: 56 min and leaf to water ratio: 1:14 (g:mL).

Primary Clarification of Stevia Extract: A Comparison Between Centrifugation and Microfiltration

Two clarification processes, namely, centrifugation and microfiltration were used for primary clarification of crude stevia extract. The optimized condition (speed and time) of centrifugation was obtained using response surface methodology. Microfiltration was carried out using a 0.2 µm pore size membrane at different operating pressures (138, 207, 276 kPa) and stirring speeds (500, 1500, and 2500 rpm). Clarified stevia extract was analyzed in terms of color, clarity, total solid, and stevioside content. The performance of both of these methods was compared based on the quality of clarified extract. 89.5% stevioside was recovered at 5334 g centrifugation speed with 25.6 minutes of centrifugation. During microfiltration, severe flux decline was observed. At 138 kPa and 500 rpm, the flux decline was 87% within 5 minutes of filtration. This was more severe at higher transmembrane pressure drop and it was 89% at 276 kPa under same stirring. Stirring speed increased the flux significantly. After 25 minutes, at 138 kPa, 2.4 times flux enhancement occurred when the stirring speed increased from 500 to 2500 rpm. Recovery of stevioside was more at lower operating pressure and about 89% recovery was attained at 138 kPa.

Growth characteristics modeling of Bifidobacterium bifidum using RSM and ANN

The aim of this work was to optimize the biomass production by Bifidobacterium bifidum 255 using the response surface methodology (RSM) and artificial neural network (ANN) both coupled with GA. To develop the empirical model for the yield of probiotic bacteria, additional carbon and nitrogen content, inoculum size, age, temperature and pH were selected as the parameters. Models were developed using ¼ fractional factorial design (FFD) of the experiments with the selected parameters. The normalized percentage mean squared error obtained from the ANN and RSM models were 0.05 and 0.1%, respectively. Regression coefficient (R2) of the ANN model showed higher prediction accuracy compared to that of the RSM model. The empirical yield model (for both ANN and RSM) obtained were utilized as the objective functions to be maximized with the help of genetic algorithm. The optimal conditions for the maximal biomass yield were 37.4 °C, pH 7.09, inoculum volume 1.97 ml, inoculum age 58.58 h, carbon content 41.74% (w/v), and nitrogen content 46.23% (w/v). The work reported is a novel concept of combining the statistical modeling and evolutionary optimization for an improved yield of cell mass of B. bifidum 255.

Optimization of HTST process parameters for production of ready-to-eat potato-soy snack

Ready-to-eat (RTE) potato-soy snacks were developed using high temperature short time (HTST) air puffing process and the process was found to be very useful for production of highly porous and light texture snack. The process parameters considered viz. puffing temperature (185–255xa0°C) and puffing time (20–60xa0s) with constant initial moisture content of 36.74% and air velocity of 3.99xa0m.s−1 for potato-soy blend with varying soy flour content from 5% to 25% were investigated using response surface methodology following central composite rotatable design (CCRD). The optimum product in terms of minimum moisture content (11.03% db), maximum expansion ratio (3.71), minimum hardness (2,749.4xa0g), minimum ascorbic acid loss (9.24% db) and maximum overall acceptability (7.35) were obtained with 10.0% soy flour blend in potato flour at the process conditions of puffing temperature (231.0xa0°C) and puffing time (25.0xa0s).

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.

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 20xa0%. 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.

Sensory Preference Modeling of Probiotic Shrikhand Employing Soft Computing

Shrikhand is a traditional fermented milk product of Indian origin that is made from fresh dahi (curd). Present investigation was carried out (1) to manufacture probiotic Shrikhand using mixed culture (1:1:1 ratio, mesophilic dahi culture NCDC-167: Bifidobacterium bifidum NCDC-255: Lactobacillus acidophilus NCDC-14), and (2) to compare its sensory quality with available market samples employing a soft computing tool, fuzzy logic to avoid market failure. Trained panel of 16 judges performed sensory evaluation. Importance of quality attributes for Shrikhand samples in general was determined as (in decreasing order): flavor, color & appearance, body & texture and mouthfeel. Observed sensory quality of the developed probiotic and control (without probiotic) samples was better than market probiotic and control samples. Strong as well as weak attributes of each sample were also determined. For developed probiotic Shrikhand, the most vital quality attribute was flavor, body & texture, followed by color & appearance, and mouthfeel. Ranking of Shrikhand samples by fuzzy logic and 9-point hedonic scale was similar.

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.