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Record W1925786118 · doi:10.1002/jsfa.7118

Sensory characterization during repeated ingestion of small‐molecular‐weight phenolic acids

2015· article· en· W1925786118 on OpenAlexafffund
Lisa M. Duizer, Allison Langfried

Bibliographic record

VenueJournal of the Science of Food and Agriculture · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFermentation and Sensory Analysis
Canadian institutionsSunnybrook HospitalSunnybrook Health Science CentreUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsIngestionSensory systemChemistryFood scienceBiochemistryBiologyNeuroscience

Abstract

fetched live from OpenAlex

BACKGROUND: Characterization of the sensory properties of small-molecular-weight phenolic acids such as ferulic and vanillic acids has been limited. The objectives of this study were to characterize the sensory perceptions of these acids and the effects of their repeated consumption on sourness, bitterness and astringency. This knowledge will further the understanding of the impact of these acids on the sensory characteristics of foods in which they are typically consumed. RESULTS: Two time-intensity sensory evaluation experiments were conducted with nine trained panelists: a single-sip study and a sequential-sip study. Concentrations of phenolic acids typically found in whole grain bread were tested. For both experiments, vanillic acid was perceived to be significantly more sour than ferulic acid, and ferulic acid was perceived to be significantly more bitter than vanillic acid. Maximum sourness, bitterness and astringency intensities significantly increased with increasing molarity for both acids. During sequential sipping, astringency and bitterness intensity increased with each sip. Sourness, however, increased to sip 3 but did not significantly increase after that point. CONCLUSION: This research demonstrates that even small quantities of phenolic acids can be perceived as increasingly bitter and astringent with repeated exposures.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.177
Threshold uncertainty score0.090

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.203
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations20
Published2015
Admission routes2
Has abstractyes

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