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Record W2150910911 · doi:10.1002/jsfa.6272

Headspace volatile components of Canadian grown low‐tannin faba bean (<i>Vicia faba</i> L.) genotypes

2013· article· en· W2150910911 on OpenAlexaffabout
B. Dave Oomah, Marion Razafindrainibe, John C. G. Drover

Bibliographic record

VenueJournal of the Science of Food and Agriculture · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetic and Environmental Crop Studies
Canadian institutionsAgriculture and Agri-Food Canada
FundersUniversité de La Réunion
KeywordsVicia fabaChemistrySolid-phase microextractionTanninCoffee beanPalatabilityFlavorGrowing seasonGas chromatography–mass spectrometryMass spectrometryChromatographyFood scienceHorticultureBotanyBiology

Abstract

fetched live from OpenAlex

BACKGROUND: As flavor is a prerequisite for palatability of new crop varieties, 13 Canadian-grown, low-tannin faba bean genotypes were evaluated for volatile components isolated with headspace solid-phase microextraction, and analyzed with gas chromatography-mass spectrometry. RESULTS: A total of 45 volatiles consisting of aldehydes, aromatic hydrocarbons, alcohols, ketones and alkanes represented on average 57, 15, 14, 5 and 4.5% of the total peak area, respectively. Total extractable volatiles were highly location dependent, whereas 10 headspace volatiles (1-pentanol, 1-hexanol, pentanal, (E)-2-heptenal, 2-ethylfuran, 2-pentylfuran, acetone, 2-butanone, 2-heptanone and 3-octen-2-one) were genotype specific and/or regulated by environmental conditions. Multivariate data analysis performed on the functional group of the volatiles using principal component analysis and cluster analysis demonstrates that the total extractable volatiles were the major factor segregating faba bean genotypes. CONCLUSION: The solid-phase microextraction technique proved to be a rapid and effective method for routine evaluation of faba bean volatile profile that may be applicable in a screening/plant breeding program.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.890
Threshold uncertainty score0.906

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 head0.010
GPT teacher head0.163
Teacher spread0.154 · 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

Citations45
Published2013
Admission routes2
Has abstractyes

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