Machine Learning Based Identification of Solvents from Post-Desiccation Patterns

Abstract

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 artificial neural network variants to determine the set of features that lead to the higher accuracy in solvent identification. We obtained an average accuracy of $96(\pm 1)\%$ considering all solvents in the analysis. The highest accuracy was obtained with sets of features that include the crack area distribution. The proposed protocol can help to determine the combination of features that optimize pattern recognition in other fields of science and engineering.

Figures (8)

• Figure 1: (a) Experimental setup components: (1) digital camera, (2) digital thermometer-hygrometer (3) aluminum container with initial mass $m_0$ of a specific mixture, and (4) analytical balance. (b) Remanent mass $m$ of the mixtures as a function of time $t$ during the drying process, for three different solvents.
• Figure 2: Fracture patterns observed in experimental samples of mixtures using solvents of a) pure liquids and b) water-ethanol mixtures with the indicated percentages of ethanol.
• Figure 3: Image processing. (a) Picture of a fracture pattern. (b) Corresponding centroids (black dots) and (c) Voronoi diagram (black edges) from the real fracture pattern (red edges) after image processing.
• Figure 4: Size-shape analysis. (A) Final patterns observed after the desiccation process of layers of cornstarch and the indicated solvent. (B) Corresponding processed images used to identify the cracks (red edges) and their centroids (black dots). In A and B the scale bar is $50\,\textup{mm}$. (a-e) Histogram distribution of (1) pattern area $A_{\textup{N}}$, (2) perimeter $P_{\textup{N}}$, (3) crack area $A_{\textup{crN}}$, (4) isoperimetric ratio $\lambda$, and (5) eccentricity $\varepsilon$, obtained using as solvent: (a) water, (b) ethanol, (c) acetone, and (d) $80/20\%$ and (e) $20/80\%$ water/ethanol. Colored lines represent the average value and the gray shaded area represents the standard deviation.
• Figure 5: Orientational ordering analysis. (A-B) Voronoi diagrams (colored cells) and centroids (white dots) obtained from the actual patterns (black lines) depending on the solvent. Colors are used to indicate A) the number of neighbors $N_B$ and B) the orientational order (here $\psi_5$ is shown for visualization) according to the color bars. (1-4) Frequency histograms of 1) nearest neighbors, and different order parameters 2) $\psi_4$, 3) $\psi_5$ and 4) $\psi_6$, depending on the solvent indicated in the same row. Colored lines represent the mean values and the gray shaded areas the corresponding standard deviations.