Non-enzymatic colorimetric detection of hydrogen peroxide using a μPAD coupled with a machine learning-based smartphone app

In the present study, iodide-mediated 3,3 ',5,5 '-tetramethylbenzidine (TMB)-H2O2 reaction system was applied to a microfluidic paper-based analytical device (mu PAD) for non-enzymatic colorimetric determination of H2O2. The proposed system is portable and incorporates a mu PAD with a machine learning-based smartphone app. A smartphone app called Hi-perox Sens capable of image capture, cropping and processing was developed to make the system simple and user-friendly. Briefly, circular mu PADs were designed and tested with varying concentrations of H2O2. Following the color change, the images of the mu PADs were taken with four different smartphones under seven different illumination conditions. In order to make the system more robust and adaptive against illumination variation and camera optics, the images were first processed for feature extraction and then used to train machine learning classifiers. According to the results, TMB + KI showed the highest classification accuracy (97.8%) with inter-phone repeatability at t = 30 s under versatile illumination and maintained its accuracy for 10 minutes. In addition, the performance of the system was also comparable to two different commercially available H2O2 kits in real samples.

Automated summary

In this study, a iodide-mediated reaction system was used to determine H2O2 colorimetrically on a microfluidic paper-based analytical device. The system incorporated a machine learning-based smartphone app, providing high accuracy and robustness against various illumination conditions.