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Record W4400040390 · doi:10.5937/ffr0-50339

Effects of spontaneous and inoculated fermentation on the total phenolic content and antioxidant activity of Cabernet Sauvignon wines and fermented pomace

2024· article· en· W4400040390 on OpenAlexaboutno aff
Nikolina Živković, Uroš Čakar, Aleksandar Petrović

Bibliographic record

VenueFood and Feed Research · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFermentation and Sensory Analysis
Canadian institutionsnot available
FundersMinistarstvo Prosvete, Nauke i Tehnološkog Razvoja
KeywordsPomaceMaceration (sewage)ChemistryFood scienceWinemakingWineFermentationAntioxidantDPPHAntioxidant capacityInoculationTroloxHorticultureBiologyBiochemistry

Abstract

fetched live from OpenAlex

The total phenolic content and antioxidant activity of wine and fermented pomace (FP) from Cabernet Sauvignon grapes harvested at the stage of full ripeness were evaluated by spectrophotometric analysis. Wine and pomace were obtained after prolonged maceration during spontaneous and inoculated fermentation of fully ripe grapes. Three individual vinifications were inoculated with the following commercial yeasts: BDX (Lallemand, Montréal, QC, Canada), FX10 (Laffort, Bordeaux, France) and Qa23 (Lallemand, Montréal, QC, Canada). For each vinification, maceration lasted 0, 3, 5, 7, 14 and 21 days, respectively. The total phenolic content was determined spectrophotometrically using the Folin-Ciocalteu method. Two different methods were used to evaluate the antioxidant activity of the wine and pomace samples: the Ferric Reducing Activity of Plasma (FRAP) and the Trolox Equivalent Antioxidant Capacity (TEAC). The use of a winemaking process that included different maceration times and inoculation with yeasts, as well as spontaneous fermentation, significantly modulated the total phenolic content of the obtained wines and FP. This study could provide a good basis for the practical application and obtaining wines with a high content of phenolic compounds and antioxidant properties.

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 score0.900
Threshold uncertainty score0.116

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 head0.059
GPT teacher head0.287
Teacher spread0.229 · 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

Citations16
Published2024
Admission routes1
Has abstractyes

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