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

Meena, Ganga Sahay; Majumdar, Gautam; Banerjee, Rintu; Kumar, Nitin; Meena, Pankaj Kumar

doi:10.1590/s1516-8913201402657

Cited by 3 publications

(2 citation statements)

References 15 publications

(20 reference statements)

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“…Maximum GABA production (7.2 g/L) was observed from Lactococcus lactis under RSM optimized conditions at 31.8°C temperature, 7.1 pH, and 15 g/L MSG [18]. Meena et al [39] studied RSM and ANN models on the growth characteristics of a mixed culture of B. bifidium and Lb. acidophilus to produce high cell biomass for commercial use.…”

Section: Discussionmentioning

confidence: 99%

Optimization and comparison of ℽ-aminobutyric acid (GABA) production by LAB in soymilk using RSM and ANN models

Rayavarapu

Tallapragada

2021

Beni-Suef Univ J Basic Appl Sci

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Background ℽ-Aminobutyric acid (GABA) is a non-proteinaceous amino acid. In the mammalian nervous system, GABA functions as an inhibitory neurotransmitter. The present study focused on screening and optimization of ℽ-aminobutyric acid (GABA) yield by lactic acid bacteria by using soymilk as basal media. Lactobacillus fermentum (Lb. fermentum) was isolated from sourdough. The qualitative confirmation of GABA production by Lb. fermentum was observed by detecting colored spots on thin layer chromatography plate (TLC) and comparing it with standard GABA spot. The GABA from bacteria is confirmed by its molecular mass using mass spectrophotometry analysis (MS analysis). Single variable experiments were conducted for various physical and nutritional parameters, and determined the GABA content produced from Lb. fermentum, viable bacterial count, and pH of the fermented soymilk medium. Experimental data were authenticated by using response surface method (RSM) and artificial neural network (ANN) model. Results The results demonstrated that through single variable experiments, the yield of GABA and the viable bacterial cells increased in soymilk containing one percent of glucose, monosodium glutamate (MSG), and inoculum volume incubated at 37 °C, 48 h at pH 5. According to RSM results, the interaction of the highest concentration of MSG (1.5%) and mid glucose concentration (1.156%) yielded maximum GABA (5.54 g/L). The experimental data were in good agreement with two optimization models. The RSM models showed less error percentage than that of the ANN model. Conclusion This study indicates that soymilk is the best basal substrate for GABA production and growth of Lb. fermentum compared to synthetic media. Lb. fermentum can be explored further by food and pharmaceutical industries for the development of functional foods and therapeutic purposes.

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Section: Discussionmentioning

confidence: 99%

Optimization and comparison of ℽ-aminobutyric acid (GABA) production by LAB in soymilk using RSM and ANN models

Rayavarapu

Tallapragada

2021

Beni-Suef Univ J Basic Appl Sci

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“…Following an analysis of the scientific literature, we learnt about the devised and proposed prognostic models on the behavior of various microorganisms in food products. There are examples of predicting the number of bacteria in raw milk, including pathogenic microorganisms (Staphylococcus aureus) and useful microflora (Bifidobacterium bifidum and Lactobacillus acidophilus) [20,31]. Earlier research mainly focused on specific kinds of microorganisms and a group of psychrotrophic microorganisms [6,7,19,27].…”

Section: Discussion Of Studying the Efficiency Of A Predicting Technimentioning

confidence: 99%

Construction of a method for predicting the number of enterobacteria in milk using artifical neural networks

Berhilevych

Касянчук

Chernetskyi

et al. 2019

EEJET

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Fundamentals and Applications of Artificial Neural Network Modelling of Continuous Bifidobacteria Monoculture at a Low Flow Rate

Dudarov

Guseva

Lemetyuynen

et al. 2022

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The application of artificial neural networks (ANNs) to mathematical modelling in microbiology and biotechnology has been a promising and convenient tool for over 30 years because ANNs make it possible to predict complex multiparametric dependencies. This article is devoted to the investigation and justification of ANN choice for modelling the growth of a probiotic strain of Bifidobacterium adolescentis in a continuous monoculture, at low flow rates, under different oligofructose (OF) concentrations, as a preliminary study for a predictive model of the behaviour of intestinal microbiota. We considered the possibility and effectiveness of various classes of ANN. Taking into account the specifics of the experimental data, we proposed two-layer perceptrons as a mathematical modelling tool trained on the basis of the error backpropagation algorithm. We proposed and tested the mechanisms for training, testing and tuning the perceptron on the basis of both the standard ratio between the training and test sample volumes and under the condition of limited training data, due to the high cost, duration and the complexity of the experiments. We developed and tested the specific ANN models (class, structure, training settings, weight coefficients) with new data. The validity of the model was confirmed using RMSE, which was from 4.24 to 980% for different concentrations. The results showed the high efficiency of ANNs in general and bilayer perceptrons in particular in solving modelling tasks in microbiology and biotechnology, making it possible to recommend this tool for further wider applications.

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

Abstract: 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.

Cited by 3 publications

References 15 publications

Optimization and comparison of ℽ-aminobutyric acid (GABA) production by LAB in soymilk using RSM and ANN models

Optimization and comparison of ℽ-aminobutyric acid (GABA) production by LAB in soymilk using RSM and ANN models

Construction of a method for predicting the number of enterobacteria in milk using artifical neural networks

Fundamentals and Applications of Artificial Neural Network Modelling of Continuous Bifidobacteria Monoculture at a Low Flow Rate

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