MétaCan
Menu
Back to cohort
Record W1969387516 · doi:10.1021/jf0705320

Identification and Quantification of a Marker Compound for ‘Pepper' Aroma and Flavor in Shiraz Grape Berries by Combination of Chemometrics and Gas Chromatography−Mass Spectrometry

2007· article· en· W1969387516 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFermentation and Sensory Analysis
Canadian institutionsnot available
FundersWine AustraliaInstitut National Du CancerMultiple Sclerosis AustraliaAlberta Water Research Institute
KeywordsAromaPepperChemometricsFlavorGas chromatography–mass spectrometryChromatographyChemistryMass spectrometryGas chromatographyFood science

Abstract

fetched live from OpenAlex

'Black pepper' aroma and flavor is important to some Australian Shiraz red wine styles but the aroma compounds involved have yet to be identified, and no objective analytical method to assess 'pepper' grape aromas is available to date. Samples of potentially 'spicy'/'peppery' grapes were obtained from vineyards in South Australia and Victoria over two vintages. The important sensory attributes of the grapes, including the aroma descriptor 'pepper', were rated by a sensory panel. The sensory study revealed a strong correlation between the intensity of 'pepper' aroma and the intensity of 'pepper' flavor perceived on the palate. The grape homogenates were analyzed by static headspace GC-MS using a cool inlet system. Vectors obtained by analysis of over 13 000 individual mass spectra per grape sample were then subjected to multivariate analyses. Both principal component analysis and partial least-squares regression were used to develop multivariate models based on mass spectra and aroma descriptors to explain the intensity of the rating of the 'pepper' character. Corresponding differences in mass spectra and aroma were observed among vineyards and from the same vineyards in different years. Additional optimization of the methodology enabled selection of a single region of the GC-MS chromatogram that allowed prediction of 'pepper' aroma intensity with a correlation coefficient >0.98 and led to the identification of alpha-ylangene, a tricyclic sesquiterpene. To assess the potential of alpha-ylangene as a marker for this sensory characteristic, a method for alpha-ylangene analysis of grapes and wine using HS-SPME-GC-MS was developed. Although not a significant aroma compound by itself, alpha-ylangene was a satisfactory marker for the 'pepper' aroma in grapes and wine, and its concentration showed similar discrimination between 'peppery' vineyards and vintages as that obtained using the multivariate models. Despite its presence in grapes, we could not detect alpha-ylangene in wine.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.620
Threshold uncertainty score0.178

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)0.0000.000

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 head0.012
GPT teacher head0.220
Teacher spread0.208 · 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