In the coffee industry, “cupping” is the process of sensorial evaluation of coffee beans, also known as sample evaluation. This process is done for three major reasons: 1) to determine the actual sensory differences between coffee samples; 2) to describe the flavors of the samples; and 3) to determine preference of product. In totality, cupping targets the measurement of the coffee's quality, which is expressed with a final numerical score. When cupping, the expert judge writes down the individual components’ scores and ranks their intensities for reference. Fuzzy logic has been employed for sensory evaluation of chhana podo (a baked dairy product), also for mango pulp and litchi juice. Moreover, a similar work exists only to train the Honduran Coffee Cuppers. This paper introduces a fuzzy expert system, AI Cupper offering an intuitive way for representing the judge's knowledge by linguistically modeling his perception of the coffee attributes through sensorial evaluation. It is capable of training cuppers when evaluating coffees from several countries and even has the capacity to learn as cupping data flows through it. The system was tested and has shown more than 95% of matching results compared with the experts’ results.

中文翻译：

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AI Cupper：一种用于对咖啡豆属性进行感官评估以得出质量评分的模糊专家系统

在咖啡行业，“拔罐”是对咖啡豆进行感官评价的过程，也称为样品评价。这个过程主要出于三个原因：1）确定咖啡样品之间的实际感官差异；2) 描述样品的风味；3) 确定产品的偏好。总的来说，杯测的目标是测量咖啡的质量，用最终的数字分数表示。拔罐时，专家评委会记下各个成分的分数并对其强度进行排名以供参考。模糊逻辑已被用于 chhana podo（一种烘焙乳制品）的感官评估，也用于芒果果肉和荔枝汁。此外，类似的工作只存在于培训洪都拉斯咖啡杯手。本文介绍了一种模糊专家系统，AI Cupper 提供了一种直观的方式来表达法官的知识，通过感官评估对他对咖啡属性的感知进行语言建模。它能够在评估来自多个国家的咖啡时训练杯测师，甚至能够在杯测数据流过时进行学习。该系统经过测试，与专家的结果相比，匹配结果达到了 95% 以上。