Machine Learning Based Identification of Solvents from Post-Desiccation Patterns
We introduce an optimized protocol of fracture pattern classification using an artificial neural network to identify the solvent involved in the desiccation cracking process of starch-liquid slurries, even after it has been completely evaporated. For this purpose, image analysis techniques were used to characterize patterns obtained from drying suspensions using single solvents (water, ethanol, acetone) and two-component solvents (water-ethanol mixtures at different concentrations). Frequency histograms were generated based on nine morphological features, taking into account their size, shape, geometry and orientational ordering. Subsequently, we used these histograms as input data into a...
| Authors | Jesús Israel Morán-Cortés, Felipe Pacheco-Vázquez |
|---|---|
| Primary category | cond-mat.soft |
| Published | 8 March 2026 |
| Last updated at source | 8 March 2026 |
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