This talk focuses on new ways for investigating sensory-instrumental data relationships using component-based methods. The proposed approaches are related to manuscripts that are in preparation at the time of this abstract submission. The proposed methods could be used in a wide range of studies, but the talk will emphasize studies involving one reference treatment and many test treatments, which is
We investigate supervised principal component regression (SPCR) its sensory and instrumental results from a data set of eight pinot noir wines. Data include measured concentrations of volatile organic compounds from headspace—solid phase micro-extraction—gas chromatography—mass spectrometry (HS-SPME-GC-MS) and sensory descriptive analysis results on two selected sensory attributes evaluated orthonasally. We show the solution from SPCR as conducted conventionally is equivalent to
Relationships between sensory and instrumental variables are often investigated using partial least squares regression (PLSR). We describe the conventional PLSR solution. Next, we describe two new approaches for conducting PLSR based on paired comparisons of objects. First, we describe the PLSR of all paired comparisons. Conducting PLSR of all paired comparisons is equivalent to the conventional solution in the sense
Supervised principal component regression (supervised PCR; SPCR) of sensory and instrumental results is conducted in new ways focusing on paired comparisons. Conventionally, SPCR is conducted on all objects. SPCR of all objects is shown to be equivalent to SPCR of all paired comparisons but different from SPCR of a subset of paired comparisons. SPCR of test-control paired comparisons is useful when the
This study examines how to carry out test-control paired comparisons after performing principal component analysis (PCA). Different approaches are proposed here, involving either centred or uncentred PCA, and their respective key properties are highlighted. In particular, we show centred PCA of test-control paired comparisons is equivalent to uncentred PCA of control-centred paired differences. It is customary to use a column-centred
Principal component analysis (PCA) is a popular technique for summarizing and exploring multivariate data sets. We propose how to conduct PCA of results from sensory studies that have a special structure, where only a subset of the product paired comparisons are of interest. We illustrate the proposed approach with two data sets, both from trained sensory panels. In the first
Principal component analysis (PCA) is often used to summarize and explore multivariate data sets, including sensory evaluation data sets. We propose how to conduct PCA of a results matrix in which only a subset of the paired comparisons is of interest. We illustrate the proposed approach with two data sets, both from trained sensory panels. In the first example, assessors