Support Vector Machine‐Based Modeling and Prediction of the Dielectric Properties of Saffron Stigma and Style

ABSTRACT Accurate and efficient separation of saffron ( Crocus sativus L .) components, particularly stigma from style, is essential for ensuring product quality and commercial value. Manual separation methods are labor‐intensive and prone to variability, necessitating automated, non‐destructive alternatives. In this study, the dielectric properties of saffron tissues were systematically investigated over a range of temperatures (20°C–60°C), moisture contents (10%–18%), and frequencies (10–1000 kHz) to assess the potential for electrostatic‐based separation. The results revealed marked dielectric contrasts between stigma and style, with notable differences in the real dielectric constant (ε'), particularly at elevated moisture levels and frequencies. Dielectric separation performance was most influenced by moisture content, followed by frequency and temperature. Three‐dimensional response surface plots and sensitivity analysis confirmed the dominance of moisture‐driven dielectric behavior, with nonlinear interactions reflecting complex physical mechanisms such as interfacial polarization. These findings underscore the intrinsic suitability of dielectric properties as reliable discriminators for saffron tissue separation. While Support Vector Machine (SVM) regression was used to model the dielectric responses, achieving high predictive accuracy (R 2 > 0.98), the core contribution of this study lies in revealing the physicochemical dielectric mechanisms that support the development of precision electrostatic separation technologies for industrial saffron processing.