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A sensory, chemical and consumer study of the peppery typicality of French gamay wines from cool-climate vineyards

2016· article· en· W2402118406 on OpenAlexfundno aff
Olivier Geffroy, Camille Buissière, Valérie Lempereur, Bertrand Chatelet

Bibliographic record

VenueOENO One · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFermentation and Sensory Analysis
Canadian institutionsnot available
FundersAlberta Water Research Institute
KeywordsVintageWineFood scienceAdvertisingChemistryPsychologyBusinessBiochemistry

Abstract

fetched live from OpenAlex

<p style="text-align: justify;"><strong>Aim</strong>: Within the protected designation of origin (PDO) Côtes d’Auvergne, Gamay N wines express unique peppery notes that may reflect high levels of rotundone. We investigated the typicality of these wines by determining their sensory, chemical and consumer profiles.</p><p style="text-align: justify;"><strong>Methods and results</strong>: Twenty-one Gamay N wines from the 2013 vintage from four French wine-growing areas were assessed by a trained sensory panel (n = 8). Principal component analysis and hierarchical clustering of olfactory data were used to describe differences among regions and to select four wines for a consumer study (n = 87). Gamay N wines from Auvergne had more intense peppery notes and higher rotundone concentrations, two characteristics that showed a significant positive correlation. The large variability in rotundone among the 12 wines from Auvergne was attributed to ethanol content, which was correlated to the rotundone levels in the wines. Those who appreciate wines with a peppery sensory profile were generally managers and professionals who are willing to pay more for a bottle of wine.</p><p style="text-align: justify;"><strong>Conclusion</strong>: There were differences in sensory profile and rotundone concentrations in Gamay N wines from cool-climate vineyards. We also identified the consumption profile of those who appreciate peppery wines.</p><p style="text-align: justify;"><strong>Significance and impact of the study</strong>: Our results provide a scientific foundation for Auvergne grape growers to promote the typicality of their wines. This research also identifies the key elements for developing the Côtes d’Auvergne wine range and adapting products to consumer profiles.</p>

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.796
Threshold uncertainty score0.813

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.232
Teacher spread0.202 · 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 designObservational
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

Citations18
Published2016
Admission routes1
Has abstractyes

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