Intensive Computational Method Applied for Assessing Specialty Coffees by Trained and Untrained Consumers

Fortunato Silva de Menezes,Gilberto Rodrigues Liska,Marcelo Ângelo Cirillo,Luiz Alberto Beijo,Flávio Meira Borém

doi:10.5772/intechopen.95234

Abstract

The sensory analysis of coffees assumes that a sensory panel is formed by tasters trained according to the recommendations of the American Specialty Coffee Association. However, the choice that routinely determines the preference of a coffee is made through experimentation with consumers, in which, for the most part, they have no specific ability in relation to sensory characteristics. Considering that untrained consumers or those with basic knowledge regarding the quality of specialty coffees have little ability to discriminate between different sensory attributes, it is reasonable to admit the highest score given by a taster. Given this fact, probabilistic studies considering appropriate probability distributions are necessary. To access the uncertainty inherent in the notes given by the tasters, resampling methods such as Monte Carlo’s can be considered and when there is no knowledge about the distribution of a given statistic, p-Bootstrap confidence intervals become a viable alternative. This text will bring considerations about the use of the non-parametric resampling method by Bootstrap with application in sensory analysis, using probability distributions related to the maximum scores of tasters and accessing the most frequent region (mode) through computational resampling methods.

Highlights

The methodology involved in the analysis of sensory data is summarized in a set of experimental and statistical techniques applied with the purpose of verifying the quality or the degree of acceptance of a given product, without, disregarding the characteristics of the individuals, with respect to your sensory skills
The following results correspond to the parameter estimates for the probability distributions fitted for the two classes of tasters, as well as the p-values referring to the validation of the probabilistic model fitted for the sensory scores
Given a level of significance of 1%, it is noted the confirmation of the fit in the sensory scores for each coffee, there is statistical evidence to assume that generalized extreme values distribution (GEV) distribution is adequate to model the maximum sensory grades of the evaluated coffees (Table 3)

Summary

Introduction

The methodology involved in the analysis of sensory data is summarized in a set of experimental and statistical techniques applied with the purpose of verifying the quality or the degree of acceptance of a given product, without, disregarding the characteristics of the individuals, with respect to your sensory skills. In this context, two distinct groups of consumers can be inserted, that is, consumers who have some enhanced sensory ability (s), resulting from product training or knowledge and totally lay consumers.

Objectives

Results

Conclusion