The eva‎luation of water hardness is a key parameter for assessing water quality across various domains, including water treatment, public health, industrial processes, agriculture, environmental monitoring, and domestic use. This study presents the development of a plasmonic colorimetric sensor array leveraging the aggregation behavior of gold nanoparticles (AuNPs) for the quantitative analysis of water hardness. The sensor array comprises three semi-selective sensing elements (SEs) based on AuNPs prepared at different pH conditions: SE1 (pH = 4.5), SE2 (pH = 6.5), and SE3 (pH = 8.5). The absorption profiles of these SEs were recorded in response to varying concentrations of calcium and magnesium ions. Upon interaction with these cations, AuNPs underwent aggregation, leading to a redshift in their absorption spectra from 520 nm to longer wavelengths, accompanied by a visible color change from red to blue. The response patterns of the SEs varied depending on the analyte type (calcium, magnesium, or their mixtures) and concentration. Machine learning algorithms, including Linear Discriminant Analysis (LDA) and Partial Least Squares Regression (PLSR), were employed to analyze the unique fingerprint response profiles of the sensor array. Dimensionality reduction using Principal Component Analysis (PCA), followed by LDA, demonstrated the method's capability to accurately distinguish between pure and mixed samples of calcium and magnesium ions across a wide concentration range. Quantification using PLSR revealed detection limits (LOD) of 1.45 ppm CaCO3and 1.40 ppm CaCO3for calcium and magnesium ions, respectively, in pure samples, with a linear range of 10–120 ppm CaCO3. For mixed samples, the method achieved an LOD of 10 ppm CaCO3 and a linear range of 6–114 ppm CaCO3 for the total ion concentration. The applicability of this approach was validated using a real well-water sample, yielding a recovery rate of 102.5% when compared to the standard ICP-OES method

نفيسه فهيمي كاشاني

محمد سراجي , اسماعيل حيدري