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    Quantification of Honey Adulteration Using a Planar Microstrip Metamaterial-Motivated Sensor and Software-Defined Radio
    (Ieee-Inst Electrical Electronics Engineers Inc, 2025) Cem Hasar, Ugur; Hasar, Hafize; Kaya, Yunus; Ozturk, Hamdullah; Korkmaz, Huseyin; Kosunalp, Selahattin; Mustafa Ramahi, Omar
    This work presents the application of a planar microstrip metamaterial (MM)-motivated sensor in the form of a split-ring resonator (SRR) for the detection and quantification of water adulteration in honey samples. The proposed sensor utilizes inexpensive software-defined radio (SDR) measurements, offering a cost-effective solution. Unlike previous MM sensors employing SRR configuration, our proposed MM-motivated sensor is integrated with a microstrip feedline and an additional vertical bar, enhancing its sensing capability. To enable accurate measurements, a simple calibration procedure based on baseline normalization is implemented, allowing for amplitude-only transmission measurements (|S-21|) using the SDR. A special sample holder was designed to increase the repeatability of measurements. We separately employ two postprocessing techniques, namely, the rolling average (RA) approach and the Savitzky-Golay (SG) filter, to effectively eliminate ripples in the measured |S-21| data obtained from the SDR. For validation and evaluation of the proposed sensor and postprocessing techniques, measurements of flower honey samples with water adulteration levels (delta ) up to 10% (mass-to-mass basis) were also performed using a vector network analyzer (VNA). The resonance frequency of |S21| is utilized as the basis for analysis. We establish a metric function (linear function) that correlates the shift in resonance frequency with delta. It is observed that this function fits to measured resonance frequency values with an R(2 )value greater than 0.96 for both postprocessing techniques with or without the sample holder. By inverting this function, we can predict delta with considerable accuracy for a given resonance frequency by using another linear function. It is observed that percentage variations between predicted and measured delta values are between 1.54% and 6.46% for testing samples with delta = 5% and delta = 7%.