Au nanozyme-based colorimetric sensor array integrates machine learning to identify and discriminate monosaccharides.

As different monosaccharides exhibit different redox characteristics, this paper presented a novel colorimetric sensor array based on the glucose oxidase-like (GOx-like) activity of Au nanoparticles (NPs) for monosaccharides identification. AuNPs can use O, ABTS, or [Ag(NH)] as an electron acceptor to catalyze the oxidation of monosaccharides in different velocity, resulting in cross-responsive signals. The current sensor array can distinguish between different monosaccharides or their mixtures through linear discriminant analysis (LDA) and hierarchical clustering analysis (HCA). Moreover, the glucose and fructose concentrations can be estimated simultaneously using a neural network regression model based on the sensor array. This method shows potential for monosaccharide detection in industrial, medical, and biological applications.