Gloppiness Phenomena and a Computer Vision Method to Quantify It

In this study, we present a rapid, cost-effective Python-driven computer vision approach to quantify the prevalent gloppiness phenomenon observed in complex fluids and gels. We discovered that rheology measurements obtained from commercial shear rheometers do show some hints, but do not exhibit a strong correlation with the extent of gloppiness. To measure the gloppiness level of laboratory-produced shower gel samples, we employed the rupture time of jetting flow and found a significant correlation with data gathered from the technical insight panelist team. While fully comprehending the gloppiness phenomenon remains a complex challenge, the Python-based computer vision technique utilizing jetting flow offers a promising, efficient, and affordable solution for assessing the degree of gloppiness for commercial liquid and gel products in the industry.

Automated summary

In this study, we propose a rapid and cost-effective computer vision approach using Python to quantify the prevalent gloppiness phenomenon observed in complex fluids and gels. We found that rheology measurements from commercial shear rheometers provide some hints, but do not strongly correlate with the extent of gloppiness. To measure the gloppiness level of laboratory-produced shower gel samples, we used the rupture time of jetting flow and found a significant correlation with data from the technical insight panelist team. While fully comprehending the gloppiness phenomenon remains complex, the Python-based computer vision technique utilizing jetting flow offers a promising, efficient, and affordable solution for assessing the degree of gloppiness in the industry.