Machine learning-assisted sensing array for simultaneous discrimination of hypochlorite and hydroxyl radicals

Abstract

Modern data analysis methods, including machine learning (ML), have become essential instruments for uncovering concealed patterns and deducing correlations that conventional analytical techniques may encounter limitations in handling. This study provides a detailed introduction to a three-dimensional sensor array for hypochlorite and hydroxyl radical developed using machine learning methods, based on the sensitive differentiation of spectroscopic properties using a single fluorescent probe. To quantitatively analyze hypochlorite and selectively identify hydroxyl radicals, the raw array signals were transformed into a synthetic index through principal component analysis (PCA). Subsequently, hierarchical clustering analysis (HCA) and density clustering analysis (DCA) were employed to establish a classifier capable of correlating data structure and similarity across various samples. The innovative approach demonstrated here not only facilitates rapid and precise identification of hypochlorite and hydroxyl radical species but also lays a sturdy foundation for accurately discerning environmental pollutants exhibiting similar properties. Moreover, by aligning with the overarching goal of cleaner production, this work contributes to the ongoing efforts aimed at mitigating environmental impacts and fostering a greener future.