Drying Uniformity, Moisture Diffusivity, and Quality Characterization of Stevia rebaudiana Leaves Using a Zigzag-Airflow Hybrid Solar Dryer

Abstract

Nonuniform moisture removal and tray-dependent quality variation remain major challenges in multi-tray drying of leafy food materials. This study characterized the drying behavior, shelf-to-shelf uniformity, moisture diffusivity, and quality attributes of Stevia rebaudiana leaves dried using a zigzag-airflow hybrid solar dryer. Drying experiments were conducted in triplicate on consecutive experimental days at three air temperatures (50°C, 60°C, and 70°C), two air velocities (1.15 and 2.25 m/s), and six tray positions. The zigzag airflow pathway was designed to redirect heated air alternately above and below successive trays, and its performance was evaluated experimentally using tray-specific moisture and quality measurements. Moisture content, moisture ratio, drying rate, effective moisture diffusivity, final-moisture uniformity, color parameters, water activity, stevioside, rebaudioside A, and total phenolic content were determined to evaluate process performance and product quality. Drying occurred entirely in the falling-rate period, indicating that internal moisture diffusion governed water removal. Increasing drying temperature and air velocity accelerated dehydration, reducing drying time from approximately 510 min at 50°C and 1.15 m/s to 210–240 min at 70°C and 2.25 m/s. Effective moisture diffusivity increased with drying intensity, confirming enhanced internal water migration at higher thermal and convective driving forces. The final moisture content showed low tray-to-tray variability, with coefficients of variation ranging from 1.56% to 4.27%, indicating high shelf-to-shelf moisture uniformity under the tested zigzag configuration. Quality characterization indicated that lower and moderate temperatures better preserved greenness, steviol glycosides, and phenolic compounds, whereas the most intensive drying condition produced the lowest water activity. Machine-learning regression further supported moisture-ratio characterization, with ensemble-based models providing accurate prediction from measurable drying variables. Overall, zigzag-airflow hybrid solar drying provides a practical approach for producing uniformly dried stevia leaves while maintaining key quality attributes.