A Comprehensive Review and Comparative Analysis of Water Hardness and Quality Detection Methods: Chromatography, E-Nose Technology, and Image-Based Techniques

Abstract

Water hardness and quality are critical factors affecting public health, industrial processes, and environmental sustainability. This comprehensive review explores the prevailing methods for detecting and evaluating water hardness and quality, with a focus on chromatography, electronic nose (E-Nose), and image-based techniques. Each of these approaches involves trade-offs among accuracy, cost, portability, and real-time monitoring capability. The E-Nose offers a cost-effective, rapid, and noninvasive approach, utilizing sensor arrays to detect volatile compounds, though it may lack the specificity of traditional methods. Image-based techniques leverage computer vision to analyze water quality by detecting color changes and turbidity, providing a visual and automated alternative. This review highlights the unique advantages and limitations of each, identifies key technological gaps, and explores recent innovations such as advanced sensors, machine learning, and sensor fusion. Through a comparative analysis, this paper evaluates the accuracy, efficiency, cost, and practical applications of these methods across different scenarios. The review highlights advances in sensor technology, the potential for integrating machine learning algorithms, the trade-offs between precision and usability in field applications. Future research directions for improving water quality monitoring and developing hybrid techniques are also discussed. Chromatography, E-Nose, and image-based methods each bring unique strengths to water quality assessment, from chromatography’s high precision in detecting specific ions to the E-Nose's rapid, portable detection of volatile compounds and image-based techniques’ costeffective, on-site analysis. Integrating these methods could create a comprehensive system combining detailed and broad-spectrum water assessments. By outlining future research directions, this work guides efforts toward more efficient, scalable, and intelligent water quality monitoring solutions.