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"Gloppiness" Phenomena and a Computer Vision Method to Quantify It.

Shijian Wu1, Mark Mintel1,2, Baran Teoman1

  • 1Colgate-Palmolive Company, Piscataway, NJ 08854, USA.

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Summary
This summary is machine-generated.

We developed a fast, affordable computer vision method using Python to measure "gloppiness" in gels and fluids. This technique, based on jetting flow rupture time, correlates well with human perception, unlike traditional rheology.

Keywords:
computer visiongloppinesspythonrheologyrupture time

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Area of Science:

  • Materials Science
  • Fluid Dynamics
  • Computer Vision

Background:

  • The phenomenon of "gloppiness" in complex fluids and gels is prevalent but difficult to quantify objectively.
  • Traditional rheology measurements show limited correlation with perceived "gloppiness".

Purpose of the Study:

  • To develop a rapid, cost-effective computer vision method for quantifying "gloppiness" in complex fluids and gels.
  • To assess the correlation between the proposed method and human perception of "gloppiness".

Main Methods:

  • A Python-driven computer vision approach was implemented.
  • Jetting flow rupture time was used as a key metric for measuring "gloppiness" in laboratory-produced shower gel samples.
  • Results were correlated with data from a technical insight panelist team.

Main Results:

  • The computer vision technique demonstrated a significant correlation with the perceived level of "gloppiness".
  • Rheology measurements from commercial shear rheometers did not show a strong correlation with "gloppiness".

Conclusions:

  • A Python-based computer vision technique utilizing jetting flow offers a promising, efficient, and affordable solution for assessing "gloppiness".
  • This method provides a valuable tool for the industry to evaluate liquid and gel products.