Topological texture analysis of microscopy images of dynamic casein gelation and its relation to rheological properties

2026-06-01 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision & Pattern Recognition · Depth: Expert, quick

Summary

A novel computational toolbox integrates Topological Data Analysis (TDA), Differential Box Counting (DBC), Multifractal Partition (MFP), and Local Binary Patterns (LBP) for analyzing time-lapse super-resolution STED microscopy images. This toolbox was applied to study 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, specifically max-Betti-1 curves, which identified 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 reflecting network rearrangements. DBC and MFP corroborated these topological transitions by resolving changes in structural complexity and spatial heterogeneity. Validated on simulated fractal images, this integrated approach offers a robust quantitative tool for characterizing complex microstructural dynamics in food and material science, providing sensitivity to subtle transitions beyond bulk rheology.

Key takeaway

For Food Scientists or Material Scientists characterizing dynamic gelation or evolving material microstructures, this integrated computational toolbox provides a more sensitive quantitative approach than bulk rheology alone. You should consider adopting this TDA-based method, which combines TDA, DBC, MFP, and LBP, to precisely track subtle microstructural transitions and protein network rearrangements in your dynamic systems, offering deeper insights into their properties.

Key insights

The toolbox combines TDA, DBC, MFP, and LBP to quantify dynamic microstructural changes in gelation, correlating with rheological properties.

Principles

• Topological loops reflect protein network interconnectivity.
• TDA max-Betti-1 curves track sol-gel transitions.
• Integrated methods enhance sensitivity to microstructural shifts.

Method

Integrate TDA (max-Betti-1 curves), Differential Box Counting, Multifractal Partition, and Local Binary Patterns on time-lapse microscopy images to analyze dynamic microstructural evolution and correlate with bulk properties.

In practice

• Analyze dynamic gelation in food science.
• Characterize evolving material microstructures.
• Quantify protein network rearrangements.

Topics

• Topological Data Analysis
• Casein Gelation
• Microscopy Image Analysis
• Rheological Properties
• Food Science
• Material Science

Code references

• Zahratabatabaei/Delifood_CV_paper

Best for: AI Scientist, Research Scientist, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.