Statistical Analysis between Analytical and Sensory Data of Coffee Aroma

Abstract

The relationship between gas chromatographic (GC) data and sensory data was analyzed in 31 arabica coffee samples by multivariate analysis, and the aroma profiles of the samples were characterized. Using principal component analysis (PCA) for GC data, the samples were classified into six groups. The relationships between the principal components obtained by PCA and sensory data were linear by Quantification Theory 1. On the basis of partial correlation coefficients, the effects of the terms used in sensory evaluation on the first and second principal component were clarified. © 1989, Japan Society for Bioscience, Biotechnology, and Agrochemistry. All rights reserved.