Hybrid Fuzzy–Eckenrode Model for Quantitative Evaluation of Fermented Cocoa Bean Quality in Ivory Coast

This study aims to develop a quantitative decision-support model for assessing the quality of fermented cocoa beans in Ivory Coast, addressing the persistent inconsistency in cocoa bean quality that undermines international competitiveness. Despite being the world’s largest cocoa producer, Ivory Coast experiences frequent quality downgrades due to weak fermentation control and limited data-driven evaluation frameworks. A descriptive–analytical quantitative approach was employed, integrating the Eckenrode and Fuzzy Eckenrode methods to transform expert judgment into measurable weights for key cocoa quality attributes—namely fat content, acidity (pH), moisture, aroma, and bitterness. Field data were collected from three major cocoa-producing regions (Yamoussoukro, Daloa, and San-Pédro) through expert evaluations and sensory analyses. The hybrid fuzzy logic model was used to enhance precision in ranking attribute importance under uncertain assessments. Results revealed that fat content (0.103), pH acidity (0.092–0.106), and moisture content (0.078–0.089) are the most influential quality determinants. Cocoa Type C exhibited the most balanced fermentation and post-harvest consistency, achieving a total attribute dispersion (ΣB = 0.139) that aligns with premium-grade standards. The fuzzy hybrid model successfully reduced subjective variability and improved ranking stability compared to classical MCDM methods. This study pioneers the application of Fuzzy Eckenrode in cocoa quality evaluation within the African agroindustrial context. By linking physicochemical and sensory parameters through quantitative weighting, it provides a replicable decision-support tool that bridges traditional post-harvest practices with data-driven quality management for sustainable cocoa competitiveness in Ivory Coast.