Wu Shijian, Mintel Mark, Teoman Baran, Jensen Stephanie, Potanin Andrei
Colgate-Palmolive Company, Piscataway, NJ 08854, USA.
Department of Chemical and Biochemical Engineering, Rutgers University, Piscataway, NJ 08854, USA.
Gels. 2023 Jun 30;9(7):532. doi: 10.3390/gels9070532.
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.
在本研究中，我们提出了一种快速、经济高效的由Python驱动的计算机视觉方法，用于量化在复杂流体和凝胶中观察到的普遍存在的“粘性”现象。我们发现，从商用剪切流变仪获得的流变学测量结果确实显示出一些线索，但与“粘性”程度没有很强的相关性。为了测量实验室生产的沐浴露样品的“粘性”水平，我们采用了喷射流的破裂时间，并发现与从技术洞察专家团队收集的数据有显著相关性。虽然全面理解“粘性”现象仍然是一项复杂的挑战，但利用喷射流的基于Python的计算机视觉技术为评估行业中商业液体和凝胶产品的“粘性”程度提供了一种有前景、高效且经济实惠的解决方案。
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