Accurate modeling and simulation of the effect of bacterial growth on the pH of culture media using artificial intelligence approaches.

This research investigates the impact of bacterial growth on the pH of culture media, emphasizing its significance in microbiological and biotechnological applications. A range of sophisticated artificial intelligence methods, including One-Dimensional Convolutional Neural Network (1D-CNN), Artificial Neural Networks (ANN), Decision Tree (DT), Ensemble Learning (EL), Adaptive Boosting (AdaBoost), Random Forest (RF), and Least Squares Support Vector Machine (LSSVM), were utilized to model and predict pH variations with high accuracy. The Coupled Simulated Annealing (CSA) algorithm was employed to optimize the hyperparameters of these models, enhancing their predictive performance. A robust dataset comprising 379 experimental data points was compiled, of which 80% (303 points) were used for training and 20% (76 points) for testing. The study focuses on three bacterial strains including Pseudomonas pseudoalcaligenes CECT 5344, Pseudomonas putida KT2440, and Escherichia coli ATCC 25,922 cultured in Luria Bertani (LB) and M63 media, across varying initial pH levels, time intervals, and bacterial cell concentrations (OD600). Key input variables for the models included bacterial type, culture medium type, initial pH, time (hours), and bacterial cell concentration, all critical to pH dynamics. Sensitivity analysis using Monte Carlo simulations revealed bacterial cell concentration as the most influential factor, followed by time, culture medium type, initial pH, and bacterial type. The dataset was rigorously validated before training to ensure its suitability for predictive modeling. Evaluation of model performance demonstrated that the 1D-CNN model exhibited enhanced predictive precision, attaining the minimal RMSE and the maximum R² values and MAPE percentages in both training and testing phases. These findings underscore the efficacy of artificial intelligence techniques, particularly 1D-CNN, in precisely predicting pH changes in culture media due to bacterial growth. This methodology provides a reliable, cost-effective, and efficient alternative to traditional experimental approaches, enabling researchers to forecast pH behavior with greater confidence and reduced experimental effort.