Ganga Sahay Meena

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.

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.

Milk protein concentrates: opportunities and challenges

Poor solubility of milk protein concentrates (MPCs) is a key deterrent factor in their wider applications in the food industry as compared to other protein-rich dried products such as casein, caseinates and whey protein concentrates and isolates. Apart from the processing factors, the protein content of a MPC also decides its solubility. Solubility is a pre-requisite property of MPCs on which its other functional properties are majorly depended. Further, there is a confusion about the term MPC itself in the literature. An attempt has been made to describe MPC and provide an understanding on the manufacture of MPCs. Further, mechanisms of insolubility, factors affecting solubility of MPCs and an insight into the recently evolved strategies for overcoming the challenges related to their poor heat stability and solubility have been reviewed. Potential applications of MPC to be utilized as a novel ingredient in food industry are also outlined.

Application of ohmic heating for concentration of milk

The concentration of milk through evaporation is the most commonly employed unit operation for the production of a wide array of traditional and industrial dairy products. Major problems associated with thermal evaporation are a loss of aroma, flavor and color change. Ohmic heating (OH) has an immense potential for rapid and uniform heating of liquid, semi-solid and particulate foods, yielding microbiologically safe and high-quality product. The effect of ohmic heating on physico-chemical, rheological, sensorial and microbial properties during concentration of cow milk, buffalo milk and mixed milk (50:50) was studied and compared to conventional evaporation. OH significantly increased free fatty acids (FFA), apparent viscosity, hydroxymethylfurfural (HMF) content, instrumental color values i.e. redness (a*) and yellowness (b*) values. However, pH value and whiteness (L*) of the concentrated milk decreased significantly. OH caused a drastic reduction in microbiological counts and treated milk can be kept for a longer period.

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.