In the coffee industry, \"cupping\" is the process of sensorial evaluations of coffee beans, also known as Sample Evaluation. This process is done for three major reasons: to determine the actual sensory differences between coffee samples, to describe the flavors of the samples, and to determine preference of product. In totality, cupping targets the measurement of the coffee's quality related to fragrance, taste, and appearance which are expressed with a final numerical score. When cupping, the expert judge writes down the individual components' scores (fragrance, aftertaste, acidity, body, etc.) and ranks their intensities for reference. Despite the fact the cuppers are using natural language statements in their judgment, they are required to use numerical values to evaluate the coffee bean attributes. Fuzzy systems allow an intuitive way of representing the judge's knowledge, by linguistically modeling the judge's perception of the coffee's attributes for sensorial evaluation of coffee-bean attributes to enhance the Specialty Coffee Association of America cupping process to derive quality scoring when grading specialty coffees. With a fuzzy expert system the judge's perception could be better assisted with a collection of linguistically expressed terms instead of numbers (complementary terms acting as shapers of the coffee bean's attribute score's gradation of meaning).
","abstract_html":"<p>In the coffee industry, "cupping" is the process of sensorial evaluations of coffee beans, also known as Sample Evaluation. This process is done for three major reasons: to determine the actual sensory differences between coffee samples, to describe the flavors of the samples, and to determine preference of product. In totality, cupping targets the measurement of the coffee's quality related to fragrance, taste, and appearance which are expressed with a final numerical score. When cupping, the expert judge writes down the individual components' scores (fragrance, aftertaste, acidity, body, etc.) and ranks their intensities for reference. Despite the fact the cuppers are using natural language statements in their judgment, they are required to use numerical values to evaluate the coffee bean attributes. Fuzzy systems allow an intuitive way of representing the judge's knowledge, by linguistically modeling the judge's perception of the coffee's attributes for sensorial evaluation of coffee-bean attributes to enhance the <em>Specialty Coffee Association of America</em> cupping process to derive quality scoring when grading specialty coffees. With a fuzzy expert system the judge's perception could be better assisted with a collection of linguistically expressed terms instead of numbers (complementary terms acting as shapers of the coffee bean's attribute score's gradation of meaning).</p>","abstract_has_math":false,"creators":["Livio, Javier A."],"institution":null,"degree_name":"Computer Science - Applied Computing Track","degree_level":"Thesis","degree_discipline":"TSYS School of Computer Science","degree_department":null,"school":null,"contributors":["Dr. Rania Hodhod","Dr. Shamim Khan","Dr. Alfredo Perez"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-11-01T07:00:00Z","date_published":"2016-11-01T07:00:00Z","updated_at":"2026-07-24T01:44:55Z","subjects":["Expert systems","Fuzzy Sets","Fuzzy Reasoning","Coffee Sensorial Evaluation","Mamdani- Style Engine","Al Cupper","Specialty Coffee Association of America","Computer Engineering","Data Storage Systems"],"languages":["English"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://csuepress.columbusstate.edu/theses_dissertations/242","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Dr. Rania Hodhod","Dr. Shamim Khan","Dr. Alfredo Perez"]},{"key":"dc:creator","label":"Author","values":["Livio, Javier A."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2017-07-25T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["TSYS School of Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Computer Science - Applied Computing Track"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Expert systems","Fuzzy Sets","Fuzzy Reasoning","Coffee Sensorial Evaluation","Mamdani- Style Engine","Al Cupper","Specialty Coffee Association of America","Computer Engineering","Data Storage Systems"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://csuepress.columbusstate.edu/theses_dissertations/242"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In the coffee industry, \"cupping\" is the process of sensorial evaluations of coffee beans, also known as Sample Evaluation. This process is done for three major reasons: to determine the actual sensory differences between coffee samples, to describe the flavors of the samples, and to determine preference of product. In totality, cupping targets the measurement of the coffee's quality related to fragrance, taste, and appearance which are expressed with a final numerical score. When cupping, the expert judge writes down the individual components' scores (fragrance, aftertaste, acidity, body, etc.) and ranks their intensities for reference. Despite the fact the cuppers are using natural language statements in their judgment, they are required to use numerical values to evaluate the coffee bean attributes. Fuzzy systems allow an intuitive way of representing the judge's knowledge, by linguistically modeling the judge's perception of the coffee's attributes for sensorial evaluation of coffee-bean attributes to enhance the Specialty Coffee Association of America cupping process to derive quality scoring when grading specialty coffees. With a fuzzy expert system the judge's perception could be better assisted with a collection of linguistically expressed terms instead of numbers (complementary terms acting as shapers of the coffee bean's attribute score's gradation of meaning).
"]},{"key":"dc:title","label":"Title","values":["Fuzzy Expert Systems: A More Human-Based Approach for Sensorial Evaluation of Coffee-Bean Attributes to Derive Quality Scoring"]}]}],"canonical_facts":{"dc:contributor":["Dr. Rania Hodhod","Dr. Shamim Khan","Dr. Alfredo Perez"],"dc:creator":["Livio, Javier A."],"dc:date.available":["2017-07-25T07:00:00Z"],"dc:description.abstract":["In the coffee industry, \"cupping\" is the process of sensorial evaluations of coffee beans, also known as Sample Evaluation. This process is done for three major reasons: to determine the actual sensory differences between coffee samples, to describe the flavors of the samples, and to determine preference of product. In totality, cupping targets the measurement of the coffee's quality related to fragrance, taste, and appearance which are expressed with a final numerical score. When cupping, the expert judge writes down the individual components' scores (fragrance, aftertaste, acidity, body, etc.) and ranks their intensities for reference. Despite the fact the cuppers are using natural language statements in their judgment, they are required to use numerical values to evaluate the coffee bean attributes. Fuzzy systems allow an intuitive way of representing the judge's knowledge, by linguistically modeling the judge's perception of the coffee's attributes for sensorial evaluation of coffee-bean attributes to enhance the Specialty Coffee Association of America cupping process to derive quality scoring when grading specialty coffees. With a fuzzy expert system the judge's perception could be better assisted with a collection of linguistically expressed terms instead of numbers (complementary terms acting as shapers of the coffee bean's attribute score's gradation of meaning).
"],"dc:identifier":["https://csuepress.columbusstate.edu/theses_dissertations/242"],"dc:language":["English"],"dc:subject":["Expert systems","Fuzzy Sets","Fuzzy Reasoning","Coffee Sensorial Evaluation","Mamdani- Style Engine","Al Cupper","Specialty Coffee Association of America","Computer Engineering","Data Storage Systems"],"dc:title":["Fuzzy Expert Systems: A More Human-Based Approach for Sensorial Evaluation of Coffee-Bean Attributes to Derive Quality Scoring"],"thesis:degree_discipline":["TSYS School of Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Computer Science - Applied Computing Track"]},"updated_at":"2026-07-24T01:44:55Z"}