The R package cata contains functions for analyzing check-all-that-apply (CATA) data from consumer and sensory tests. Cochran’s Q test, McNemar’s test, and Penalty-Lift analysis are provided; for details, see Meyners, Castura & Carr (2013). Cluster analysis can be performed using b-cluster analysis, then evaluated using various measures; for details, see Castura, Meyners, Varela & Næs (2022). Consumers can also be
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 two equivalent ways to investigate these results using PCA. The first is a centred PCA of column-centred test-control
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 assessors 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 L1 norm where the median absolute
In sensory evaluation, consumers often evaluate products by answering check-all-that-apply (CATA) questions. These questions instruct the consumer to check all terms in a list that describe the product, where checking a term is called a citation. When all products are evaluated by all consumers under two conditions, the CATA data can be organized into a four-dimensional array (assessors, products, terms,
Organizer: Sara R. Jaeger, Aarhus University, Denmark.Organizer & Speaker 1. Daniele Asioli, University of Reading, United Kingdom.Speaker 2. Fabien Llobell, Lumivero, France.Speaker 3. Chris Berry, Colorado State University, USA.Roundtable Discussant 1. John Castura, Compusense Inc., Canada.Roundtable Discussant 2. Veronika Jones, Symrise, Germany.Roundtable Discussant 3. Phillipa Bailey, CRG Global, United Kingdom.Roundtable Moderator: Marija Banovic, Aarhus University, Denmark. Jaeger, S.R., Asioli, D.,
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 assessors 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 L1 norm where the median absolute
Explore the intersection of Artificial Intelligence (AI) and sensory science in this cutting-edge workshop designed for professionals, researchers, and innovators in food science, consumer research, product development, and quality assurance. Join Compusense’s Bryson Bolton and John Castura in an informative discussion into how machine learning and AI technologies can enhance traditional sensory and consumer research methodologies. Bolton, B., & Castura,
Sensory studies aim to understand how products (e.g. food and beverages) are perceived and conceptualized. Measuring products analytically provides information about their physical and chemical properties. It is useful for product developers to have a more complete understanding which physical and chemical properties are associated with which sensations. Component-based methods are a popular way of exploring relationships between sensory and
It is often of interest to understand the relationship between predictor and response variables and to predict the sensory response from the instrumental data. We investigated three ways of conducting supervised principal component regression (SPCR) of instrumental and sensory data. First, we describe the SPCR of all objects. We show SPCR of all paired comparisons is equivalent to SPCR of
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