High sugar intake from commercial beverages is a public health concern, motivating rapid, user-friendly tools for sugar quantification. We present a compact planar microwave metamaterial sensor that estimates sugar concentration by monitoring resonant frequency shifts induced by dielectric loading. Tests with aqueous glucose solutions demonstrated a wide dynamic range (0 to 12,000 mg/dL), perfect linearity (R² = 1), and high repeatability. Validation on two commercial beverages showed sensor-predicted sugar contents consistent with their nutrition labels. The method is reagent-free, tolerates opaque samples, and operates under ambient conditions, making it suitable for on-site consumer use as well as regulatory inspection and quality-control applications.

This paper presents the development and analysis of a planar microwave sensor designed for detecting adulteration in milk by evaluating the purity of milk in a water-based solution. The sensor comprises a pair of complementary split-ring resonators (CSRRs) fabricated on an FR4 substrate, measuring 30 mm × 50 mm × 1.6 mm, with a dielectric constant of 4.4 and a loss tangent of 0.022. The device’s performance was assessed using a vector network analyzer (VNA) by varying the ratio of full-cream milk to water in a 50 mL solution, starting from 60% and increasing in 10% increments up to 100%. Measurements focused on return loss (RL) at resonant frequency 1.5425 GHz, which exhibited minimal frequency shifts but significant variations in RL magnitude with changing milk concentration (M). To establish a mathematical relationship between RL and M, we segmented the data into two ranges—60% to 80% and 80% to 100% milk concentrations—and applied second-order polynomial regression for each segment. The quadratic equations derived from this regression allowed us to express M in terms of RL. Verification of this method was conducted using arbitrary samples of milk concentrations: 61%, 62%, 72%, 88%, 93%, and 95%. Discrepancies between different quadratic solutions for the same RL values were resolved by normalizing the return losses against pure water and comparing the resulting normalized values with those from known concentrations. This comparison allowed for the accurate selection of the appropriate quadratic equation based on the closest match. Our normalization approach revealed distinct patterns correlating RL magnitudes, enabling us to select the appropriate quadratic equation segment based on minimal discrepancies. The analysis confirmed that by excluding negative and complex solutions and solutions which are beyond the stipulated range of the curve segment, the accuracy of the sensor in determining milk concentration exceeded 83.5%. This study demonstrates the potential of the proposed microwave sensor in ensuring milk quality by effectively quantifying milk purity.