A. Gomez, J. Raj, G. Korstjens, N. Alsbou, M. Khandaker
University of Central Oklahoma,
United States
Keywords: food safety, sensor, microwave, ripening, spectroscopy
Summary:
Introduction: Monitoring food ripening and freshness is essential to maintain quality and reduce waste across the food supply chain [1]. Conventional methods—such as gas chromatography, near-infrared spectroscopy, and microbial assays—are often destructive, costly, and confined to laboratory use, limiting real-time assessment during storage or retail display [2-3]. To address this challenge, a microwave-based, non-invasive scanner was developed to detect and quantify food ripening by analyzing changes in dielectric properties. As moisture, sugar, and biochemical composition evolve during ripening, the dielectric constant shifts accordingly. This system provides a rapid, low-cost, and portable approach for continuous monitoring of fruits, vegetables, and other perishable foods. Materials and Methods: The prototype integrates a MegiQ VNA0440 Vector Network Analyzer (0.4–4.0 GHz) with 4×4 and 8×8 multistatic antenna arrays that transmit and receive electromagnetic waves interacting with food samples (Fig. 1a). Reflection and transmission data (S-parameters) were acquired under controlled conditions using lemon (Fig. 1b). A Python-based automation system synchronized antenna switching through an Arduino-controlled HMC321ALP4E RF network, enabling fast sequential scanning and data logging. Calibration used the OSLT (Open, Short, Load, Through) method. Data were processed in MATLAB using Hann window filtering, frequency averaging, and 2D back-projection imaging to visualize compositional changes. Validation tests were performed on lime and apple samples to monitor moisture loss and biochemical transformations during ripening. Results: The scanner detected clear dielectric changes across ripening stages. Lime samples exhibited consistent dielectric shifts in the 2.0–3.5 GHz range, corresponding to moisture and biochemical variation. The 8×8 antenna setup provided greater spatial resolution and stronger signal-to-noise ratios than the 4×4 configuration. Automated scanning reduced total acquisition time from 12 min 30 s to 3 min 20 s, achieving a fourfold speed improvement. Microwave images (Fig. 1 c and Fig. 1d) reconstructed from back-projection showed distinct internal contrasts between unripe and ripened states. Results from six repeated trials demonstrated reproducibility with <5% deviation, confirming the method’s stability. The scanner also differentiated artificially accelerated ripening from natural processes, validating its compositional sensitivity. Discussion: Microwave sensing proved to be a rapid and non-destructive method for ripening detection. Dielectric variations correlated with internal moisture migration and starch-to-sugar conversion—key biochemical indicators of maturity. Compared to optical or chemical tests, this technique provides deeper penetration, faster response, and minimal handling. The multistatic antenna design enhanced scanning efficiency and image quality, while Python automation reduced operator error. Future improvements include integrating an RF circulator, compact signal generator, and touchscreen interface for a handheld field-deployable system suitable for food retail use. Significance: This research demonstrates a scalable, microwave-based food freshness and ripening detection system that enables real-time, non-invasive quality control across the food value chain. The technology can complement smart packaging solutions like ENF Foods’ ENLiner, enhancing monitoring of fruits and vegetables during distribution. Beyond reducing food waste and improving safety, this project contributes to Oklahoma’s innovation ecosystem in agri-food sensing, materials science, and applied microwave engineering while providing hands-on research and workforce training for undergraduate students.