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
This tutorial surveys some of the most frequently used methods for exploring multivariate data sets fromsensory and consumer science. Component-based methods are often applied for data reduction andvisualization of results.The tutorial is divided into three parts.In Part 1, we contrast principal component analysis (PCA) with principal variable analysis, then discussmultiple factor analysis (MFA), which is often used to investigate multiblock
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