In sensory evaluation, principal component analysis (PCA) is often used to explore differences between products. In some studies, there is one control product (e.g. a reference or benchmark) and many test products, where test-control paired differences are of primary interest. We discovered there are two equivalent ways to investigate these results using PCA. The first approach is a centred PCA
Gin is a distilled spirit characterized by juniper flavor, often accompanied by other botanical flavors. The objective of this research was to use gin as a case study to compare temporal dominance of sensations (TDS), temporal check‐all‐that‐apply (TCATA), and temporal ranking (TR). Trained panelists used each of these methods to evaluate the temporal profile of six gins (standardized to 30%
A unified framework is provided for analysing check-all-that-apply (CATA) product data following the “one citation, one vote” principle. CATA data arise from studies where A consumers evaluate P products by describing samples by checking all of the T terms that apply. Giving every citation the same weight, regardless of the assessor, product, or term, leads to analyses based on the
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
Gin is a distilled spirit characterized by juniper flavor, often accompanied by other botanical flavors. The objective of this research was to use gin as a case study for comparing temporal temporal dominance of sensations (TDS), temporal check-all-that-apply (TCATA), and temporal ranking (TR). Trained panelists used each of these methods to evaluate the temporal profile of six gins (standardized to
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
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
Chocolate-flavored milk contributes to excessive intake of added sugars among children and adolescents, which why it is a good candidate product for sucrose replacement. This study investigates how replacing sucrose partially or completely with different sweetener systems affects the sensory profile and consumer liking. Five chocolate-flavored milk treatments were formulated, varying in sucrose replacement level (partial: 58%; complete: 100%) and
Test-control paired comparisons can be investigated after principal component analysis (PCA). We show that a centred PCA of test-control paired comparisons is equivalent to an uncentred PCA of control-centred results. We demonstrate how to conduct these analyses. We illustrate key properties of centred and uncentred PCA. We justify why coordinates of control-centred results can be visualized in principal components obtained