Three-way principal component analysis applied to noodles sensory data analysis

The results presented in this paper are issued from the study and the interpretation of a 3-way data matrix constituted from the sensory analysis of wheat noodles. The aim of this work was to provide a complementary understanding of internal relationships between the chemical composition of the noodles and sensory attributes such as color, surface smoothness, elasticity or chewiness. The application of the Tucker3 algorithm involving the noodles composition, the sensory attributes and the assessors as the three modes, facilitates the interpretation of the differences among the types of noodles and also the estimation of the effect of different sources of variability on the sensory evaluation. A joint interpretation of the first and of the second mode (noodles and sensory attributes) allows to link appearance and texture attributes with the composition of the noodles. (C) 2010 Elsevier B.V. All rights reserved.