On-site colorimetric food spoilage monitoring with smartphone embedded machine learning.

Real-time and on-site food spoilage monitoring is still a challenging issue to prevent food poisoning. At the onset of food spoilage, microbial and enzymatic activities lead to the formation of volatile amines. Monitoring of these amines with conventional methods requires sophisticated, costly, labor-intensive, and time consuming analysis.

Colorimetric detection of HO with FeO@Chi nanozyme modified µPADs using artificial intelligence.

Peroxidase mimicking FeO@Chitosan (FeO@Chi) nanozyme was synthesized and used for high-sensitive enzyme-free colorimetric detection of HO. The nanozyme was characterized in comparison with  FeO nanoparticles (NPs) using X-ray diffraction, Fourier-transform infrared spectroscopy, dynamic light scattering, and thermogravimetric analysis. The catalytic performance of FeO@Chi nanozyme was first evaluated by UV-Vis spectroscopy using 3,3',5,5'-tetramethylbenzidine.

A field-deployable water quality monitoring with machine learning-based smartphone colorimetry.

Water quality monitoring is an increasing global concern as the pollution of water sources causes adverse effects on economic growth and human health. Traditional approaches to the detection of pollutants are time-consuming and labor-intensive due to the requirement of sophisticated equipment or laboratory settings. Therefore, portable devices featuring rapid response and easy operation are indispensable in water quality monitoring.

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.

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)-HO reaction system was applied to a microfluidic paper-based analytical device (μPAD) for non-enzymatic colorimetric determination of HO. The proposed system is portable and incorporates a μPAD with a machine learning-based smartphone app. A smartphone app called "" capable of image capture, cropping and processing was developed to make the system simple and user-friendly.