Texture Identification in Liquid Crystal-Protein Droplets using Evaporative Drying, Generalized Additive Modeling, and K-means Clustering

TL;DR

This study combines image texture analysis, statistical modeling, and clustering to characterize and classify liquid crystal-protein droplet textures during drying, revealing stage-specific patterns and demonstrating the method's potential for rapid analysis.

Contribution

It introduces an integrated approach using texture features, GAM, and K-means clustering to analyze LC-protein droplet textures, highlighting stage-specific differences and the method's effectiveness.

Findings

Distinct textures identified at different drying stages

All texture features significantly differentiate LC-protein droplets

Final drying stage shows well-defined, non-overlapping clusters

Abstract

Sessile drying droplets manifest distinct morphological patterns, encompassing diverse systems viz., DNA, proteins, blood, and protein-liquid crystal (LC) complexes. This study employs an integrated methodology that combines drying droplet, image texture analysis (features from First Order Statistics, Gray Level Co-occurrence Matrix, Gray Level Run Length Matrix, Gray Level Size Zone Matrix, and Gray Level Dependence Matrix), and statistical data analysis (Generalized Additive Modeling and K-means clustering). It provides a comprehensive qualitative and quantitative exploration by examining LC-protein droplets at varying initial phosphate buffered concentrations (0x, 0.25x, 0.5x, 0.75x, and 1x) during the drying process under optical microscopy with crossed polarizing configuration. Notably, it unveils distinct LC-protein textures across three drying stages: initial, middle, and final. The Generalized Additive Modeling (GAM) reveals that all the features significantly contribute to differentiating LC-protein droplets. Integrating the K-means clustering method with GAM analysis elucidates how textures evolve through the three drying stages compared to the entire drying process. Notably, the final drying stage stands out with well-defined, non-overlapping clusters, supporting the visual observations of unique LC textures. Furthermore, this paper contributes valuable insights, showcasing the efficacy of drying droplets as a rapid and straightforward tool for characterizing and classifying dynamic LC textures.