Optimization of Escherichia coli shake flask culture medium by artificial neural network / 中国生物制品学杂志

@#Objective To optimize a shake flask culture medium for Escherichia coli(E.coli)with high biomass and viability using artificial neural networks(ANN). Methods Using the proportion of glucose(Glu),yeast extract(YE),yeast peptone(YP),soy peptone(SP)and yeast nitrogen base(YNB)as the mixture component,and the A_(600)(A1)value of cell suspension,wet bacterial weight(G,g/L)of culture and cell viability(A2,A_(460))as the response values,the mixture design was used to screen the mixture components that had a significant effect on the response value. The ANN model was constructed with the test results of mixture design as training and verification data samples. The input variables were mixture components and restricted the upper and lower limits of the mixture components,and the output variables were mixture design response values. The optimized medium formula and reference values were obtained by the constructed ANN. The medium formula was further adjusted by Monte Carlo simulation to obtain the shake flask medium formula of E.coli,which was then verified for 10 times. Results The shake flask culture medium of E.coli was composed of Glu 26 g/L,SP 26 g/L,YNB13 g/L with the total concentration of 65 g/L. The verification results showed that the probability of A1 ≥ 14 was 60%,the probability of G ≥ 77 g/L was 50% and the probability of A2 ≥ 11 was 40%. The mean values of the incubation result data were equivalent to the reference values. Conclusion The shake flask culture medium of E.coli optimized in this study can obtain E.coli with high biomass and bacterial activity.

Estimating Hydroxymethyfurfural (HMF) Concentration Via Modified Seliwanoff Test Using Artificial Neural Network (ANN)

Abstract Hydroxymethylfurfural (HMF) is a quality indicator, especially in foods where changes in protein-carbohydrate interactions are observed during the applied process. In this study absorbance and L*, a*, b* values of red color emerged due to the relationship between hydroxymethylfurfural (HMF) and resorcinol during the modified Seliwanoff test were used as input data artificial neural network (ANN) to determine the HMF concentration for the first time. A linear relationship, between HMF concentration and absorbance of red color, can be represented by equation absorbance = 0.0020 + 0.0012* concentration of HMF (mg L-1) with R2 = 99.6%, Fisher ratio: 0.18, p value of lack of fit: 0.975, correlation coefficient: 0.9960. Intra-day and inter-day precision expressed as relative standard deviation (RSD) %, were 2.35 - 3.65% and 3.16 - 4.73%, respectively. Recovery rates and RSDs were in the range of 99.34 - 100.47% and 1.58 - 3.68%. It showed high correlation compared to HPLC method used as reference method (0.998). The R2 values of ANN for estimation of HMF concentration were found 0.90 for training, 0.96 for validation, and 0.99 for testing and AARD was found 8.85%. Evaluation of the absorbance and L*, a*, b* values of the red color with artificial intelligence is a reliable way to determine the HMF concentration.

Obstructive sleep apnoea detection using convolutional neural network based deep learning framework

This letter presents an automated obstructive sleep apnoea (OSA) detection method with high accuracy, based on a deep learning framework employing convolutional neural network. The proposed work develops a system that takes single lead electrocardiography signals from patients for analysis and detects the OSA condition of the patient. The results show that the proposed method has some advantages in solving such problems and it outperforms the existing methods significantly. The present scheme eliminates the requirement of separate feature extraction and classification algorithms for the detection of OSA. The proposed network performs both feature learning and classifies the features in a supervised manner. The scheme is computation-intensive, but can achieve very high degree of accuracy—on an average a margin of more than 9% compared to other published literature till date. The method also has a good immunity to the contamination of the signals by noise. Even with pessimistic signal to noise ratio values considered here, the methods already reported are not able to outshine the present method. The software for the algorithm reported here can be a good contender to constitute a module that can be integrated with a portable medical diagnostic system.

Optimisaton techniques: a futuristic approach for formulating and processing of pharmaceuticals.

Designing and formulating an ideal pharmaceutical product is a very tedious job for a formulator as it comprises of multiple objectives. The traditional method followed for years is not only expensive and time consuming but it also needs lot of effort to be put in and in spite of that at times it proves to be unfavourable and unpredictable too.The recent approach to optimise i.e. to achieve the best combination of product and process characteristics under the given conditions is by using Design of Experiment (DoE).Design of Experiment (DoE) is an organised approach to determine the relationship between the factors (Xs) affecting a process and the output of that process (Ys).The recent optimisation methodologies include various approaches that come under the 2 main categories namely, simultaneous optimisation and sequential optimisation. Nowadays there are various software available to carry out the optimisation of pharmaceutical products.

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.

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 » 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 percent, 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 percent (w/v), and nitrogen content 46.23 percent (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.