International audienceSensory profiles are classically summed up by a Principal Component Analysis (PCA) performed on the table of means crossing products and descriptors. This paper proposes a way for evaluating the variability around the variables' representation in PCA. It is thus possible to have an idea of the uncertainty of the position of each variable. To do this, the resampling methods used take into account the special nature of sensory data
The x, y, and z axes are the first, second, and third components that together capture most of the v...
Principal components analysis (PCA) is a multivariate ordination technique used to display patterns ...
A statistical method for analysing sensory profiling data obtained by means of fixed vocabulary or f...
This article presents a discussion of principal components analysis of descriptive sensory data. Foc...
International audiencePrincipal Component Analysis (PCA) of product mean scores is generally used to...
Principal Component Analysis (PCA) of product mean scores is generally used to obtain a product map ...
International audiencePrincipal component analysis (PCA) has its origin in psychology, where it was ...
One of the problems in analyzing sensory profiling data is to handle the systematic individual diffe...
This paper presents a discussion of principal components analysis of descriptive sensory data. Focus...
International audienceAlthough Principal Component Analysis (PCA) of product mean scores is most oft...
<p>(A) Percent variance explained by each of the six principal components. (B) Variable correlation ...
<p><b><i>A</i></b>, first and second principal components. <b><i>B</i></b>, first and third principa...
Usually, in pattern recognition problems we represent the observations by mean of measures on approp...
Temporal check-all-that-apply (TCATA) data can be summarized and explored using principal component ...
This paper describes a method of disentangling different sources of variance contributing to compone...
The x, y, and z axes are the first, second, and third components that together capture most of the v...
Principal components analysis (PCA) is a multivariate ordination technique used to display patterns ...
A statistical method for analysing sensory profiling data obtained by means of fixed vocabulary or f...
This article presents a discussion of principal components analysis of descriptive sensory data. Foc...
International audiencePrincipal Component Analysis (PCA) of product mean scores is generally used to...
Principal Component Analysis (PCA) of product mean scores is generally used to obtain a product map ...
International audiencePrincipal component analysis (PCA) has its origin in psychology, where it was ...
One of the problems in analyzing sensory profiling data is to handle the systematic individual diffe...
This paper presents a discussion of principal components analysis of descriptive sensory data. Focus...
International audienceAlthough Principal Component Analysis (PCA) of product mean scores is most oft...
<p>(A) Percent variance explained by each of the six principal components. (B) Variable correlation ...
<p><b><i>A</i></b>, first and second principal components. <b><i>B</i></b>, first and third principa...
Usually, in pattern recognition problems we represent the observations by mean of measures on approp...
Temporal check-all-that-apply (TCATA) data can be summarized and explored using principal component ...
This paper describes a method of disentangling different sources of variance contributing to compone...
The x, y, and z axes are the first, second, and third components that together capture most of the v...
Principal components analysis (PCA) is a multivariate ordination technique used to display patterns ...
A statistical method for analysing sensory profiling data obtained by means of fixed vocabulary or f...