Open AccessFUZZY LOGIC AND CLUSTER ANALYSIS IN THE QUALITY OF THE BEVERAGE FROM CONILON COFFEE
Author(s) -
Julião Soares de Souza Lima,
Samuel de Assis Silva
Publication year - 2018
Publication title -
coffee science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.268
H-Index - 15
eISSN - 1984-3909
pISSN - 1809-6875
DOI - 10.25186/cs.v13i4.1467
Subject(s) - mathematics , fuzzy logic , sensory analysis , euclidean distance , coffea canephora , statistics , food science , horticulture , biology , computer science , coffea arabica , artificial intelligence , geometry
The quality of coffee beverages has been under study due to the demand of the consumer market for both arabica and conilon coffee. The aim of this work was to study beverage quality from different clones by means of sensory analysis, in 13 clones of the variety Victoria INCAPER 8142 produced at average altitudes of 100.0 m and 528.0 m and with the cherry fruits processed by natural drying or depulping. Fuzzy classification was adopted for the global scores obtained in the sensory analysis, on a scale of 70.0 to 100.0 points, with the Euclidean distance from the cluster analysis being used to define the dissimilarity between the global fuzzified scores for the different clones at the two altitudes and for the two methods of processing the fruit. Clones C4 and C10, at the intermediate maturation stage, presented a mean global score (GS) of 85.0 points for the coffee produced at the altitude of 528.0 m and for the depulped fruit, corresponding to a degree of fuzzy pertinence (FI) of 0.50, and being classified as fine coffee. These same clones presented dissimilarities in the beverage produced by the depulped fruit, with better quality for the coffee at the higher altitude. The fuzzy classification taken together with the cluster analysis to interpret the mean global scores (GS) in the sensory analysis of the beverage for the different treatments under study identified variation in beverage quality