Non-enzymatic colorimetric glucose detection based on Au/Ag nanoparticles using smartphone and machine learning.

Conventional enzyme-based glucose quantification approaches are not feasible due to their high cost, specific working temperatures, short shelf life, and poor stability. Therefore, a portable platform, which offers rapid response, cost-efficiency, and high sensitivity, is indispensable for the healthcare of diabetes. In this study, we proposed a portable platform incorporating gold (Au) and silver (Ag) nanoparticles (NPs) with a smartphone application based on machine learning for non-enzymatic glucose quantification. The color change obtained from the reaction of small and large Au/Ag NPs with glucose was captured using a smartphone camera to create a dataset for the training of machine-learning classifiers. Our custom-designed user-friendly smartphone application called "GlucoQuantifier" uses a cloud system to communicate with a remote server running a machine-learning classifier. Among the tested classifiers, linear discriminant analysis exhibits the best classification performance (93.63%) with small Au/Ag NPs and it demonstrates that incorporating Au/Ag NPs with machine learning under a smartphone application can be used for non-enzymatic glucose quantification.