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.
Keywords: computer vision; gloppiness; python; rheology; rupture time.