This work presents the design, fabrication, and experimental validation of a compact dual-band microstrip sensor for accurate characterization of oil–water mixtures. The sensor, implemented on an FR-4 substrate with overall dimensions of 10.94 × 14.92 mm2, operates at two distinct resonant frequencies, 1.2 GHz and 14.92 GHz, enabling precise dielectric analysis across a wide frequency spectrum. The dual-mode configuration enhances sensitivity, selectivity, and resolution, facilitating the reliable detection of subtle variations in the relative permittivity of test samples. Experimental measurements were performed on oil–water mixtures with purity levels ranging from 0 to 100%, prepared in 5% increments under controlled laboratory conditions to ensure repeatability and accuracy. The resulting resonance shifts and S-parameter responses were analyzed using a Radial Basis Function (RBF) neural network trained to predict water concentration based on extracted microwave features, including resonant frequencies, magnitude responses, quality factors, and phase characteristics. The trained network achieved a coefficient of determination (R2) greater than 0.99, with a mean square error (MSE) of 3.24 (%2) and a mean relative error (MRE) of 3.6%, indicating outstanding predictive precision and robustness. The proposed sensor exhibited sensitivities of 73.5 MHz/εᵣ at 1.2 GHz and 101.48 MHz/εᵣ at 14.92 GHz, demonstrating significantly higher performance compared to other similar designs in the literature. By combining compact geometry, high sensitivity, and machine-learning-based analysis, this work introduces a reliable and intelligent sensing platform with strong potential for real-time industrial and environmental applications requiring accurate dielectric-based fluid composition analysis.
Dehkalani et al. (Tue,) studied this question.