Sensorial analysis of categorized data of special coffee to identify similar crop seasons pairs using Kappa

Abstract

This paper presents the proposal of a statistical method to analyse dependent agreement data with categorical ordinal responses for a longitudinal study in sensorial analysis of special coffee. The assessment of sensory attributes of special coffees were carried out by certified raters using a continuous scale of grades. The approach aimed to applying data categorization methods commonly used in machine learning which generated not only a concise summary of continuous attributes to describe the data but also allowed to maximize the agreement grades in a longitudinal study. A previous analysis was carried out to identify the similarity of grades in all sample unities. The categorization allowed the construction of marginal models for all distinct pairs time observed in the longitudinal study for modeling the concordance correlationskappa. It also enabled to conclude that samples of harvests related to yellow grain fruits have similar sensorial characteristics. Higher altitudes are significantly favorable to obtain samples with similar sensorial characteristics identifying the set of covariates which contributed either in positive or negative way while estimating kappa.