We propose using the current-voltage (I−V) characteristics at the interface between the eutectic gallium-indium alloy (eGaIn) electrode and the hydrogel to detect Streptococcus thermophilus and Bacillus coagulans bacterial concentrations in the range from 10^4 to 10^8 CFU/mL. We apply the feed-forward deep neural network model (multilayer perceptron) for logical data analysis. And training such a machine learning model is performed by an error backpropagation algorithm.
Лаврентьев Ф.В., Иванов А.С., Шиловских В.В. (науч. рук. Скорб Е.В.) Soft Hydrogel Actuator for Machine-Learning Assisted Bacteria Detection // Сборник тезисов докладов конгресса молодых ученых. Электронное издание. – СПб: Университет ИТМО, [2022]. URL: https://kmu.itmo.ru/digests/article/7585