Machine learning-assisted liquid crystal-based aptasensor for the specific detection of whole-cell Escherichia coli in water and food.

We have developed a rapid, facile liquid crystal (LC)-based aptasensor for E. coli detection in water and juice samples. A textile grid-anchored LC platform was used with specific aptamers adsorbed via a cationic surfactant, cetyltrimethylammonium bromide (CTAB), on the LC surface. The presence of E. coli dissociates the aptamers from CTAB and restores the dark signal induced by the surfactant. Using polarized microscopy, the images of the LCs in the presence of various concentrations of E. coli were captured and analyzed using image analysis and machine learning (ML). The artificial neural networks (ANN) and extreme gradient boosting (XGBoost) rendered the best results for water samples (R = 0.986 and RMSE = 0.209) and juice samples (R = 0.976 and RMSE = 0.262), respectively. The platform was able to detect E. coli with a detection limit (LOD) of 6 CFU mL.