Identification of Water-Soluble Polymers through Machine Learning of Fluorescence Signals from Multiple Peptide Sensors

Abstract

Recently, there has been growing concern about the discharge of water-soluble polymers (especially synthetic polymers) into the environment. Therefore, the identification of water-soluble polymers in water samples is becoming increasingly crucial. In this study, a chemical tongue system to simply and precisely identify water-soluble polymers using multiple fluorescently responsive peptide sensors was demonstrated. Fluorescence spectra obtained from the mixture of each peptide sensor and water-soluble polymer were changed depending on the combination of the polymer species and peptide sensors. Water-soluble polymers were successfully identified through the supervised or unsupervised machine learning of multidimensional fluorescence signals from the peptide sensors.