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Related Concept Videos

Capillarity in Fluid01:19

Capillarity in Fluid

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Capillarity describes the movement of liquid in small spaces without external forces acting on it. The capillarity is driven by surface tension and adhesive interactions between the liquid and surrounding solid surfaces. This effect is often seen in narrow tubes, porous materials, and fine particles.
Surface tension is crucial to capillarity. It results from cohesive forces between liquid molecules at the liquid-air boundary, forming a skin that resists external forces. When the capillary tube...
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A Microfluidic Chip for the Versatile Chemical Analysis of Single Cells
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Capillary Flow Profile Analysis on Paper-Based Microfluidic Chips for Classifying Astringency Intensity.

Daesik Son1,2, Junseung Bae1,3, Chanwoo Park1,3

  • 1Department of Biosystems Engineering, Seoul National University, Seoul 08826, Republic of Korea.

Sensors (Basel, Switzerland)
|August 28, 2025
PubMed
Summary
This summary is machine-generated.

Quantifying astringency intensity using paper microfluidics and machine learning is now possible. This method analyzes polyphenol-mucin aggregation via capillary flow dynamics for portable field analysis.

Keywords:
astringency intensitycapillary profilemachine learningmicrofluidic paper-based analytical device

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Area of Science:

  • Food Science
  • Biochemistry
  • Analytical Chemistry

Background:

  • Astringency, a complex oral sensation, arises from polyphenol-mucin interactions.
  • Quantifying astringency in field settings is challenging.
  • Capillary action and surface tension offer a potential measurement approach.

Purpose of the Study:

  • To develop a portable method for quantifying astringency intensity.
  • To utilize paper-based microfluidics and machine learning for analyzing polyphenol-mucin aggregation.
  • To assess capillary flow dynamics as an indicator of astringency.

Main Methods:

  • Replication of tannic acid (TA)-mucin aggregation on a paper-based microfluidic chip.
  • Analysis of capillary flow dynamics using machine learning (ML) models.
  • Smartphone-based data acquisition with a standardized sample loading system.

Main Results:

  • Higher TA concentrations resulted in increased aggregation and reduced capillary flow rates.
  • The support vector machine (SVM) model achieved 95.2% accuracy in classifying astringency levels.
  • A simplified feature extraction method using a single coefficient showed comparable classification performance.

Conclusions:

  • The developed method provides a simple, cost-effective approach for quantifying astringency.
  • This technique has potential for development into a portable system for field analysis.
  • The study demonstrates the feasibility of using ML and microfluidics for sensory analysis.