Title : ( Support Vector Machine‐Based Modeling and Prediction of the Dielectric Properties of Saffron Stigma and Style )
Authors: Seyyed Meisam Mazloumzadeh , M. Hossein Abbaspour-Fard , Abbas Rohani ,Access to full-text not allowed by authors
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 perfor-mance 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 ehavior, with nonlinear interactions re-flecting 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.
Keywords
dielectric constant ; electrostatic separation ; regression ; saffron stigma ; support vector machine@article{paperid:1105709,
author = {Mazloumzadeh, Seyyed Meisam and Abbaspour-Fard, M. Hossein and Rohani, Abbas},
title = {Support Vector Machine‐Based Modeling and Prediction of the Dielectric Properties of Saffron Stigma and Style},
journal = {Journal of Food Process Engineering},
year = {2025},
volume = {48},
number = {1},
month = {November},
issn = {0145-8876},
pages = {70274--70289},
numpages = {15},
keywords = {dielectric constant ; electrostatic separation ; regression ; saffron stigma ; support vector machine},
}
%0 Journal Article
%T Support Vector Machine‐Based Modeling and Prediction of the Dielectric Properties of Saffron Stigma and Style
%A Mazloumzadeh, Seyyed Meisam
%A Abbaspour-Fard, M. Hossein
%A Rohani, Abbas
%J Journal of Food Process Engineering
%@ 0145-8876
%D 2025
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