Proposal of a metric selection index for correspondence analysis: an application in the sensory evaluation of coffee blends

Costa, Adilson Silva da; Resende, Mariana; Nakano, Eduardo Yoshio; Cirillo, Marcelo Ângelo; Borém, Flávio Meira; Ribeiro, Diego Egídio

doi:10.5433/1679-0359.2020v41n2p479

Cited by 1 publication

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“…As an example of these techniques, Costa et al (2018) used a new approach for calculating the coordinates in a correspondence analysis by incorporating residues applied to coffee bean data. Costa et al (2020) proposed a metric selection index for the identification of a statistic that determines the most appropriate metric in correspondence analysis, preventing subjectivity in the interpretation of similarities between the types of blends and grades.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Canonical correspondence Analysis: Description of the data of Coffee

Santos

Cirillo

Borém

et al. 2023

Semin., Ciênc. Exatas Tecnol.

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The formulation of coffee blends is of paramount importance for the coffee industry, as it provides the product with an expressive ability to compete in the market and adds sensory attributes that complement the consumption experience. Through redundancy analysis and canonical correspondence analysis, it is possible to study the relationships between a set of sensory notes and a set of blends with different proportions of coffee variety through multivariate linear regression models. However, it is unrealistic to assume that such sensory responses are given linearly in relation to the formulation of the blends, since some coffee species have greater weight in the sensory evaluation (quadratic terms) and the effect of the mixtures (term of interaction). With this motivation, this work aims to propose the use of redundancy analysis and nonlinear correspondence analysis through multivariate polynomial regression to evaluate the acceptance of different varieties of coffee blends according to the scores given by the evaluators. Finally, it is concluded that there were gains in the percentage of total explained variance in the polynomial models in relation to the classic models.

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Section: Introductionmentioning

confidence: 99%