Growth characteristics modeling of Bifidobacterium bifidum using RSM and ANN

Abstract

The aim of this work was to optimize the biomass production by Bifidobacterium bifidum 255 using the response surface methodology (RSM)

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

“…GA is a tool by which the optimization problems can be accurately solved with in a limited use of computer time (Das 2005). Optimization of various bacterial strains in Erlenmeyer flasks using different optimization tools have been reported by several other authors (Nagarjun et al 2005;Kumari et al 2009;Lima et al 2009;Negi and Banerjee, 2009;Usmiati and Marwati, 2009;Coelho et al 2011;Meena et al 2011;Meena et al 2013). This study developed the empirical model to increase the cell growth of the mixed culture of B. bifidum and L. acidophilus (1:1 ratio) by optimizing the growth parameters such as temperature, pH, inoculum volume, incubation period and additional effect of different carbon and nitrogen sources employing RSM, ANN and GA.…”

“…The methods used for the microbial culture activation and pellet extraction was same as earlier reported (Meena et al 2011;Meena et al 2013). Composition of the medium used for the growth of mixed culture of B. bifidum and L. acidophilus (1:1) contained (g/L) casein peptone 10, yeast extract 5, sodium acetate 5, Tween 80 1, MgSO 4 0.2, MnSO 4 0.05, K 2 HPO 4 2 and its pH was maintained at 7.0.…”

“…With the help of factorial design, different s values were identified as (s 1 = 0, s 2 =0), (s 1 = 0, s 2 =1), (s 1 = 1, s 2 =0), and (s 1 = 1, s 2 =1). All the experiments were conducted according to s 1 = 0 and s 2 =0 design (Meena et al 2011), during present investigation.…”

“…In this present investigation, a feed forward back propagation neural network (Meena et al 2011;Meena et al 2013) was used to evaluate its capability in cell mass yield prediction of mixed culture. In this process, ANN computed the error between the desired (predicted) response and the actual (experimental) response.…”

“…The number of neurons in input layer, hidden layer and output layer of this neural network were kept as 6, 11 and 1, respectively. This ANN was first trained with reported data of B. bifidum (Meena et al 2011). After training, it was able to predict the cell mass yield of the mixed culture accurately through error minimization that was compared with the predicted value of cell mass yield obtained from RSM (Meena et al 2013).…”