Capillary Flow Profile Analysis on Paper-Based Microfluidic Chips for Classifying Astringency Intensity

Astringency, a complex oral sensation resulting from interactions between mucin and polyphenols, remains difficult to quantify in portable field settings. Therefore, quantifying the aggregation through interactions can enable the classification of the astringency intensity, and assessing the capillary action driven by the surface tension offers an effective approach for this purpose. This study successfully replicates tannic acid (TA)-mucin aggregation on a paper-based microfluidic chip and utilizes machine learning (ML) to analyze the resulting capillary flow dynamics. Aggregates formed by mixing mucin with TA solutions at three concentrations showed that higher TA levels led to greater aggregation, consequently reducing the capillary flow rates. The flow dynamics were consistently recorded using a smartphone mounted within a custom 3D-printed frame equipped with a motorized sample loading system, ensuring standardized experimental conditions. Among eight trained ML models, the support vector machine (SVM) demonstrated the highest classification accuracy at 95.2% in distinguishing the astringency intensity levels. Furthermore, fitting the flow data to a theoretical capillary flow equation allowed for the extraction of a single coefficient as an input feature, which achieved comparable classification performance, validating the simplified feature extraction strategy. This method was also feasible even with only a portion of the initial data. This approach is simple and cost-effective and can potentially be developed into a portable system, making it useful for field analysis of various liquid samples.