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

• A machine learning based platform is proposed for glucose determination with a μ PAD. • A smartphone app was developed to perform machine learning in colorimetric analysis. • Over 98% accuracy was obtained in glucose determination with TMB. • First study to link machine learning, μ PAD and smartphone for glucose determination. • The platform has great potential for non-invasive measurement of glucose. Potassium iodide (KI) and 3,3 ′ ,5,5 ′ -tetramethylbenzidine (TMB) are frequently used as chromogenic agents in μ PADs for glucose determination. Chitosan (Chi) has peroxidase like activity and improves the analytic performance of μ PADs when used in combination with a chromogenic agent. Here, a portable platform incorporating a μ PAD with a smartphone application based on machine learning was developed to quantify glucose concentration in artificial saliva. The detection zones of the μ PAD were modified with three different detection mixtures containing; (i) KI, (ii) KI+Chi and (iii) TMB. After the color change, the images of the μ PADs 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. [ABSTRACT FROM AUTHOR]