Application of deep neural techniques in predictive modelling for the estimation ofEscherichia coligrowth rate

Abstract

Aims To develop a predictive model for Escherichia coli using deep neural networks. Methods and Results Batch experiments are conducted at different temperatures closer to optimum value (36·5°C, 37°C, 37·5°C, 38°C and 38·5°C) to obtain the growth curves of E .coli K‐12. Two primary models namely modified Gompertz and new logistic are chosen. Three secondary models namely Gaussian, nonlinear autoregressive eXogenous (NARX) model and long short‐term memory (LSTM) are developed. The novelty in this paper is the development of secondary models using artificial neural network (ANN) and deep network. The performance measures chosen to compare the developed primary and secondary models are correlation coefficient (R2), root‐mean‐square error (RMSE) and accuracy factor (Af). Results show that modified Gompertz model has better R2 (0·99) and RMSE (0·019) when compared to new logistic model. Also, the deep network model outperforms other secondary models. Based on the primary and novel secondary model, a predictive model (tertiary model) is developed with improved accuracy and is validated. Conclusions The proposed predictive model exhibit good validation results in terms of RMSE and R2 values and can be applied for determining the growth rate of E. coli at a particular temperature value. Significance and Impact of the Study The proposed model can be used in food processing industries during enzyme production such as Chymosin, to predict the growth rate of E. coli as a function of temperature. Also, the developed LSTM and NARX models can be used to predict maximum specific growth rate of other microbial strains with proper training.

Predicting Single Cell Lag Time and Maximum Specific Growth Rate of Proteus mirabilis using Curve Fitting Machine Learning Algorithm (MLA)

“…22,23 However, no machine learning-based curve fitting has yet to be developed that can predict singlecell lag times of foodborne pathogens, which is important for future application of machine learning in microbiological risk evaluation. [24][25][26][27] The objective of this study was to predict the maximum specific growth rate of the asymptote and the maximum value reached (µ m ), and the lag time (l) of Proteus mirabilis. The Logistic model is fitted to the growth data of Proteus mirabilis and MLA were used to train and validate the model, so that it can accurately predict various unseen data of Proteus mirabilis.…”