Resolution of binary mixtures of microorganisms using electrochemical impedance spectroscopy and artificial neural networks

This work describes the resolution of binary mixtures of microorganisms using electrochemical impedance spectroscopy (EIS) and artificial neural networks (ANNs) for the processing of data. Pseudomonas aeruginosa, Staphylococcus aureus and Saccharomyces cerevisiae were chosen as models for Gram-negative bacteria, Gram-positive bacteria and yeasts, respectively. In this study, best results were obtained when entering the imaginary component of the impedance at each frequency (strongly related to the capacitive elements of the electrical equivalent circuit) into backpropagation neural networks made up by two hidden layers. The optimal configuration of these layers respectively used the radbas and the logsig transfer functions with 4 or 6 neurons in the first hidden layer and 10 neurons in the second one. In all cases, good prediction ability was obtained with correlation coefficients better than 0.989 when comparing the predicted and the expected values for a set of six external test samples not used in the training process.