Sensory profiles and preference analysis in ornamental horticulture: The case of the rosebush

The context of ornamental horticulture is considered in order to extend the techniques of sensory and preference evaluation by taking the rosebush as a plant model. In a preliminary study (Boumaza, Demotes-Mainard, Huché-Thélier, & Guérin, 2009), a sensory evaluation was conducted in order to set up a list of attributes. Subsequently, this list was adapted to assess 10 rosebushes. After the control of the panel performance using a multivariate strategy of analysis, the average scores were used in product mapping. The evaluation of the preferences with regard to these rosebushes was undertaken: 253 subjects were asked to rank the products by decreasing order of liking. Thereafter, the preference data were subjected to an internal preference mapping and a cluster analysis. Six homogeneous segments of consumers were eventually retained. By way of performing an external preference mapping, the average ranks were regressed upon the sensory attributes using principal component regression: the preferences of 67% of the consumers were satisfactorily explained by the attributes

Chimiométrie appliquée à la spectroscopie infrarouge. Introduction à la chimiométrie

chap. 10National audienc

Multiple-correspondence analysis optical microscopy for determination of starch granules

International audienc

Application of latent root regression for calibration in near-infrared spectroscopy. Comparison with principal component regression and partial least squares

International audienc

Finding and explaining clusters of consumers using the CLV approach

In consumer studies, liking scores for a set of products are usually collected from a panel of consumers. When additional information is available both on products and consumers, the data can be organized in an L-shaped structure. The CLV (Clustering around Latent Variables) approach which was originally designed to identify segments of consumers according to their preferences is extended in order to take account of product characteristics data or/and consumer background information

Chimiométrie appliquée à la spectroscopie infrarouge. Méthodes exploratoires

chap. 11National audienc

Continuum redundancy-PLS regression: A simple continuum approach

The relationships between two data sets are investigated. The aim is to predict one data set from the other. New formulations of Redundancy Analysis and Partial Least Square Regression (PLS) are discussed, clearly showing the connexions between these two popular methods. Moreover, it is shown that the Redundancy Analysis and PLS regression are the two end points of a continuum approach. Properties related to this continuum approach are discussed, showing how the multicolinearity problem is handled. The interest of the general strategy of analysis is illustrated on the basis of a data set pertaining to epidemiology.

SORT-CC: A procedure for the statistical treatment of free sorting data

National audienceA statistical approach for the analysis of free sorting data is discussed. In a first stage, the sorting data from each subject are arranged into a dataset consisting of indicator variables which reflect the memberships of the stimuli to the groups formed by the subject under consideration. Thereafter, an appropriate standardization is applied on these data and a three way statistical method, namely Common Components and Specific Weights Analysis, is performed on the datasets thus obtained. This makes it possible to take account of the individual differences among the subjects and to depict graphical displays showing the relationships among the stimuli on the one hand and among subjects on the other hand. The general strategy of analysis is illustrated on the basis of a dataset and the outcomes are compared to those of other strategies of analysis of sorting data

SORT-CC: A procedure for the statistical treatment of free sorting data

International audienceA statistical approach for the analysis of free sorting data is discussed. In a first stage, the sorting data from each subject are arranged into a dataset consisting of indicator variables which reflect the memberships of the stimuli to the groups formed by the subject under consideration. Thereafter, an appropriate standardization is applied on these data and a three way statistical method, namely Common Components and Specific Weights Analysis, is performed on the datasets thus obtained. This makes it possible to take account of the individual differences among the subjects and to depict graphical displays showing the relationships among the stimuli on the one hand and among subjects on the other hand. The general strategy of analysis is illustrated on the basis of a dataset and the outcomes are compared to those of other strategies of analysis of sorting data

Principal component regression, ridge regression and ridge principal component regression in spectroscopy calibration

International audienc