Smart Fuzzy Cupper: Employing approximate reasoning to derive coffee bean quality scoring from individual attributes

摘要

This paper presents a fuzzy expert system, an enterprise system designed and developed under the category of software as a service (SaaS) to grade specialty coffees from several countries. The system uses approximate reasoning and inner libraries to dynamically construct fuzzy rules, making the system capable of learning as cupping data flows through it. The coffee individual attributes' scores are linguistically expressed through sliders optimally designed to ease data gathering, encouraging the coffee judge to use words instead of numbers (low, medium, high and very high). Results from testing the system show more than 95% of matching results compared to the experts' evaluations.