Using Single Free Sorting and Multivariate Exploratory Methods to Design a New Coffee Taster's Flavor Wheel
Open accessAbstract
The original Coffee Taster's Flavor Wheel was developed by the Specialty Coffee Assn. of America over 20 y ago, and needed an innovative revision. This study used a novel application of traditional sensory and statistical methods in order to reorganize the new coffee Sensory Lexicon developed by World Coffee Research and Kansas State Univ. into scientifically valid clusters and levels to prepare a new, updated flavor wheel. Seventy‐two experts participated in a modified online rapid free sorting activity (no tasting) to sort flavor attributes of the lexicon. The data from all participants were compiled and agglomeration hierarchical clustering was used to determine the clusters and levels of the flavor attributes, while multidimensional scaling was used to determine the positioning of the clusters around the Coffee Taster's Flavor Wheel. This resulted in a new flavor wheel for the coffee industry.
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The Coffee Brewing Control Chart omits the wide variety of sensory attributes possible in coffee.
“Perhaps most importantly, the chart omits the wide variety of sensory attributes possible in coffee.”
From Brew temperature, at fixed brew strength and extraction, has little impact on the sensory profile of drip brew coffee (2020) · cites this paper as [7]
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- Authors
- Spencer, M., Sage, E., Velez, M., & Guinard, J.
- Affiliations
- University of California, Davis (US)
- Specialty Coffee Assoc. of America 117 W. 4th St, Suite 300 Santa Ana CA 92701 U.S.A
- Corresponding author
- Spencer, Molly
- Venue
- Journal of Food Science
- Publisher
- Wiley
- Published
- 2016-11-11
- Volume
- 81
- Issue
- 12
- Pages
- S2997-S3005
- ISSN
- 0022-1147 (ISSN-L)
- 1750-3841
- PMC
- PMC5215420
- OpenAlex
- W2553773726
- Cited by
- 103
- References
- 18
- Citations by year
- 2026: 11 · 2025: 19 · 2024: 14 · 2023: 14 · 2022: 16 · 2021: 11 · 2020: 12 · 2019: 4 · 2017: 2
- FWCI
- 2.52
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Food Science (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- Specialty Coffee Asscn. of America
- World Coffee Research
- Topics
- Life Sciences › Agricultural and Biological Sciences › Food Science › Sensory Analysis and Statistical Methods
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Categorization, perception, and language
- Health Sciences › Medicine › Pharmacology › Coffee research and impacts
- Keywords
- hierarchical clustering, multidimensional scaling, coffee industry
- MeSH terms
- Coffee, Food Handling, Food Technology, Fruit, Humans, Kansas, Odorants, Taste, Reproducibility of Results, Multivariate Analysis, Cluster Analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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