Colorimetric detection of H2O2 with Fe3O4@Chi nanozyme modified µPADs using artificial intelligence.

Peroxidase mimicking Fe3O4@Chitosan (Fe3O4@Chi) nanozyme was synthesized and used for high-sensitive enzyme-free colorimetric detection of H2O2. The nanozyme was characterized in comparison with  Fe3O4 nanoparticles (NPs) using X-ray diffraction, Fourier-transform infrared spectroscopy, dynamic light scattering, and thermogravimetric analysis. The catalytic performance of Fe3O4@Chi nanozyme was first evaluated by UV-Vis spectroscopy using 3,3',5,5'-tetramethylbenzidine. Unlike Fe3O4NPs, Fe3O4@Chi nanozyme exhibited an intrinsic peroxidase activity with a detection limit of 69 nM. Next, the nanozyme was applied to a microfluidic paper-based analytical device (µPAD) and colorimetric analysis was performed at varying concentrations of H2O2 using a machine learning-based smartphone app called "Hi-perox Sens++ ." The app with machine learning classifiers made the system user-friendly as well as more robust and adaptive against variation in illumination and camera optics. In order to train various machine learning classifiers, the images of the µPADs were taken at 30 s and 10 min by four smartphone brands under seven different illuminations. According to the results, linear discriminant analysis exhibited the highest classification accuracy (98.7%) with phone-independent repeatability at t = 30 s and the accuracy was preserved for 10 min. The proposed system also showed excellent selectivity in the presence of various interfering molecules and good detection performance in tap water.

Colorimetric food spoilage monitoring with carbon dot and UV light reinforced fish gelatin films using a smartphone application.

The objective of this study was to develop novel colorimetric films for food freshness monitoring. UV light irradiation (365 nm) and carbon dots (CDs) were tested as the potential crosslinkers in the fabrication of anthocyanins doped fish gelatin (FG) films. The effect of crosslinkers on the optical, surface, structural, barrier and mechanical properties of FG films was investigated. The incorporation of CDs under UV irradiation improved the tested properties of FG films. The kinetic colorimetric responses of FG films against ammonia vapor were studied to simulate the food spoilage and determine the ammonia sensitivity of the films. Among the tested films, UV-treated FG films containing 100 mg/l (FG-UV-CD100) indicated the best properties. Later, the color difference of FG-UV-CD100 films was observed to correlate well with microbial growth and TVB-N release in skinless chicken breast samples. At the same time, a custom-designed smartphone application (SmartFood) was developed to be used with the FG-UV-CD100 film for quantitative estimation of food freshness in real-time. The proposed food freshness monitoring platform reveals a great potential to minimize global food waste and the outbreak of foodborne illness.

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 (µPAD) for non-enzymatic colorimetric determination of H2O2. The proposed system is portable and incorporates a µPAD 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 µPADs were designed and tested with varying concentrations of H2O2. Following the color change, the images of the µPADs 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.