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Machine Learning-Enabled Image Analysis of Complex Chemical Mixtures: Synthetic Urine Droplets as a Test System.

Amrutha S V¹, Beni B Dangi², Oliver Steinbock¹

¹Florida State University, Department of Chemistry and Biochemistry, Tallahassee, Florida 32306-4390, United States.

Analytical Chemistry

|July 3, 2026

Summary

Image analysis of dried urine stains can infer chemical composition, particularly pH, using machine learning. While effective for some components like NaCl, predicting multiple solute concentrations in complex mixtures remains challenging due to data limitations.

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CRISPR-Cas-mediated Multianalyte Synthetic Urine Biomarker Test for Portable Diagnostics

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Published on: December 8, 2023

Area of Science:

Analytical Chemistry
Materials Science
Biophysics

Background:

Dried droplet deposits exhibit self-organization during evaporation, encoding chemical information.
This phenomenon presents opportunities for developing low-cost sensing technologies for chemical analysis.

Purpose of the Study:

To assess the feasibility of inferring solute concentrations and pH from images of dried synthetic urine stains.
To explore the application of automated experimentation and machine learning for chemical sensing.

Main Methods:

Utilized a robotic droplet imager (RODI) and Latin hypercube sampling for efficient data generation.
Generated 70,000 images across two systems: a urea-NaCl model and a seven-component synthetic urine mixture.
Reduced images to 47 descriptors capturing morphology and texture, followed by Random Forest regression analysis.

Main Results:

Achieved useful predictive performance for NaCl concentration in the two-component system.
Identified pH as the most reliably inferred parameter in the complex seven-component synthetic urine mixture.
Limited success in continuous prediction of individual solute concentrations in complex mixtures, potentially due to high-dimensional data sparsity.

Conclusions:

Image-based inference of chemical composition from dried droplets shows promise, particularly for pH sensing.
The study highlights current capabilities and information-theoretic limitations for complex chemical mixture analysis.
Further research may be needed to overcome data sparsity challenges for accurate multi-solute prediction.