In 2021, approximately 537 million people were diagnosed with diabetes mellitus. With rates expected to rise, health expenditures are projected to reach one trillion USD by 2030. Thus, measuring glucose levels is essential for rationalizing the costs of public health systems. In this context, this paper presents two major contributions. First, it demonstrates that using deionized water (DI-water) as a reference for glucose sensing is not a reliable approach for representing human blood plasma (BP), as it lacks ions and suppresses essential effects such as losses. As an alternative, we investigate the use of an artificial blood plasma solution (ABPS) that closely resembles real human BP. Characterized over a range from 500 MHz to 10 GHz, ABPS shows marginal differences in real permittivity but significant differences in imaginary permittivity compared to DIwater. The second contribution is the design of a highly sensitive microwave resonator sensor based on concentric double circular split ring resonator (DCCSRR) on a ROGERS 5880 TM substrate. This sensor can differentiate glucose concentrations from 0 to 400 mg/dL, exceeding the relevant range for diabetic individuals (50-300 mg/dL). The DCCSRR operates at 2.48 GHz and can detect minimal concentration variations of 25 mg/dL in low concentrations, representing a significant advancement in the field. Differently from most sensitive approaches available to date, this structure operates in a non-licensed band and in a fully passive form, offering flexibility for implementation and low cost. These characteristics position it as a state-of-the-art solution in microwave glucose sensors. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

(This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.)In this work, we combine microwave dielectric spectroscopy and machine learning techniques to assess the authenticity of extra virgin olive oil (EVOO). We investigate four common adulterants of EVOO, namely soybean oil, corn oil, sunflower oil, and canola oil. We use a low-cost spiral shaped microwave resonator-based sensor operating at 546.8 MHz to detect changes in the complex permittivity of oil samples. A vector network analyzer (VNA) is used to extract complex scattering parameters S11 and S21 that serves as inputs for two artificial neural network models. The first model, using only the real and imaginary parts of S21, achieves an overall accuracy of 95.8% in predicting the applied adulterant in test samples. In contrast, the second model, incorporating the real and imaginary parts of both S11 and S21, attains a 100% accuracy for test samples. Additionally, we investigate the relationship between the measured |S21| (in dB) and the adulteration level, expressed as the percentile value of the volume of adulterant per volume of the sample (mL/mL). For each adulterant, a calibration equation is developed using partial least squares regression (PLSR) to predict adulteration levels up to 50%. The maximum root mean square error (RMSE) is 2.1% for canola oil adulteration prediction. PLSR yields RMSE values of 0.9% for soybean oil, 1.1% for corn oil, and 1.0% for sunflower oil adulteration. This methodology offers both qualitative and quantitative analyses of EVOO, capable of identifying adulterations as low as 5% with a simple, portable, and practical system.