Machine learning-based colorimetric determination of glucose in artificial saliva with different reagents using a smartphone coupled [formula omitted]PAD

Abstract

Potassium iodide (KI) and 3,3′,5,5′-tetramethylbenzidine (TMB) are frequently used as chromogenic agents in μPADs for glucose determination. Chitosan (Chi) has peroxidase like activity and improves the analytic performance of μPADs when used in combination with a chromogenic agent. Here, a portable platform incorporating a μPAD with a smartphone application based on machine learning was developed to quantify glucose concentration in artificial saliva. The detection zones of the μPAD were modified with three different detection mixtures containing; (i) KI, (ii) KI+Chi and (iii) TMB. After the color change, the images of the μPADs were taken with four different smartphones under seven different illumination conditions. The images were first processed for feature extraction and then used to train machine learning classifiers, resulting in a more robust and adaptive platform against illumination variation and camera optics. Different machine learning classifiers were tested and the best machine learning classifier for each detection mixture was obtained. Next, a special application called “GlucoSensing” capable of image capture, cropping and processing was developed to make the system more user-friendly. A cloud system was used in the application to communicate with a remote server running machine learning classifiers. Among the three different detection mixtures, the mixture with TMB demonstrated the highest classification accuracy (98.24%) with inter-phone repeatability under versatile illumination.