2025/06/23 by S Amrutha, Bruno C. Batista, Beni B. Dangi +1 · 1 voice · 1 citation
Engineering · Environmental Science · #Advanced Chemical Sensor Technologies #Biosensors and Analytical Detection #Water Quality Monitoring Technologies
paper · doi:10.1021/acs.est.5c05770
openalex publication_date 2025/06/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/04
Water hardness is critical in domestic and industrial systems, typically measured by titration, atomic absorption spectroscopy, or inductively coupled plasma mass spectrometry. Here, we present a novel, photo-based method for quantifying water hardness using deposit patterns formed during the evaporation of hard water mixed with highly concentrated solutions of the reporter salts NaHCO 3 and NaCl. This indirect approach enables the detection of hardness levels as low as 25 ppm, an improvement by a factor of 1,400 over earlier stain-based studies. Our method leverages high-resolution imaging, automated sample preparation, and advanced image analysis, including machine learning, to detect subtle morphological changes in the deposit patterns. A robotic drop imager (RODI) generated over 16,000 images, from which 47 geometric and textural metrics were extracted to train high-dimensional linear regression models and multilayer perceptron (MLP) networks. Using synthetic water samples with hardness levels of up to 250 ppm, we achieved prediction accuracies exceeding 91% for sodium bicarbonate-based patterns. Extending this methodology, we validated the methodology on real-world tap water, demonstrating its potential as a cost-effective alternative to traditional techniques and a versatile tool for water quality monitoring.