0000000000976347
AUTHOR
Michel Visalli
Principal component analysis versus canonical variate analysis. For the analysis of sensory profiles. Meta-analysis of 387 sensory datasets
International audience; Principal Component Analysis (PCA) of product mean scores is generally used to generate a product map from sensory profiling data. This approach does not take into account variance of these product mean scores due to individual variability. Canonical Variate Analysis (CVA) of the product effect in the two-way (product*panelist) multivariate ANOVA model is the natural extension of the classical univariate approach. This analysis generates successive components maximizing the ANOVA F-criterion. However, CVA requests the inversion of a covariance matrix which can result in computing instability when the sensory attributes are highly correlated. The paper compares result…
Modelling the early determinants of food preferences in the OPALINE cohort
Diaporama confidentielDiaporama confidentiel; The OPALINE project aimed at understanding the determining factors of the development of food preferences and eating behaviour up to the age of 2 years by following a cohort of children with a longitudinal recording of perinatal and postnatal feeding experiences, of children’s sensitivity to food tastes and odours and of parental feeding practices. The aim was to conjointly analyse the datasets to draw an overall picture of these potential determinants of food preferences and of their relative weight over the course of the first two years.The recruitment of a cohort of children (N=314) was conducted thanks to the help of local health and childho…