Topological texture analysis of microscopy images of dynamic casein gelation and its relation to rheological properties
2026-06-01 • Artificial Intelligence
Artificial IntelligenceComputer Vision and Pattern Recognition
AI summaryⓘ
The authors created a new computational method that combines several advanced image analysis tools to study how a protein gel forms over time. They used this method on detailed microscope images showing the gelation of sodium caseinate at different temperatures and acid levels. Their approach tracked the creation and change of tiny loop structures in the protein network, which matched key phases of gel formation seen in other tests. This combined technique also detected subtle changes in the gel's microstructure that traditional methods average out. Overall, their toolbox helps better understand and measure complex material changes during gel formation.
Topological Data AnalysisDifferential Box CountingMultifractal PartitionLocal Binary PatternsSTED microscopySodium caseinateGelationMax-Betti-1 curvesProtein networkSol-gel transition
Authors
Zahra Tabatabaei, Diana Soto Aguilar, Jose C. Bonilla, Mathias P. Clausen, Jon Sporring
Abstract
We propose a novel computational toolbox that integrates Topological Data Analysis (TDA), Differential Box Counting (DBC), Multifractal Partition (MFP), and Local Binary Patterns (LBP), applied to time-lapse super-resolution STED microscopy images of sodium caseinate gelation induced by glucono-delta-lactone (GDL) at 30 °C and 40 °C and two GDL concentrations (1.8% and 3.5% w/v). TDA tracked topological loops, closed ring-like structures reflecting protein network interconnectivity, via max-Betti-1 curves, which revealed a lag phase of dispersed aggregates, a sharp decay coinciding with network percolation and the rheologically observed sol-gel transition, and a post-gelation increase corresponding to network rearrangements. These topological transitions were corroborated by DBC and MFP as these methods were able to resolve changes in structural complexity and spatial heterogeneity. The toolbox was validated on simulated fractal images prior to experimental application. Together, these descriptors provided sensitivity to subtle microstructural transitions that bulk rheology captured as averaged bulk mechanical responses. This integrated approach provides a robust quantitative tool for characterizing complex microstructure in food and material science with evolving microstructural dynamics. Code is available at https://github.com/Zahratabatabaei/Delifood_CV_paper.git