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Record W4220900655 · doi:10.1038/s41598-022-07925-7

Influence of freezing and heating conditions on grape seed flavan-3-ol extractability, oxidation, and galloylation pattern

2022· article· en· W4220900655 on OpenAlexaff
Joshua VanderWeide, Filippo Del Zozzo, Esmaeil Nasrollahiazar, James A. Kennedy, E. Peterlunger, Laura Rustioni, Paolo Sabbatini

Bibliographic record

VenueScientific Reports · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFermentation and Sensory Analysis
Canadian institutionsUniversity of British Columbia
FundersCollege of Engineering, Michigan State UniversityMichigan State University
KeywordsBrowningFreezing toleranceChemistryVitis viniferaRipeningFood scienceFlavanProanthocyanidinGrape seedCultivarFrost (temperature)ViticultureBotanyWineHorticultureBiologyBiochemistryPolyphenolAntioxidantOrganic chemistry

Abstract

fetched live from OpenAlex

In cool-climate viticulture, the short growing season can influence grape seed maturation by reducing the apparent oxidation of flavan-3-ol monomers and associated increase in seed browning. A reduction in seed maturation increases the potential extraction of flavan-3-ol monomers into wine during maceration operations, heightening bitterness. Here, we carried out a 2 × 2 factorial experiment to test the ability of freezing and heating treatments to advance maturation (decrease flavan-3-ol, improve browning) of (Vitis vinifera L.) Pinot noir and Cabernet Sauvignon seeds over a 24-h incubation period. Only freezing significantly increased seed browning in both cultivars. Subsequent correlations with seed flavan-3-ol monomer concentrations suggest that freezing enhanced the oxidation of these compounds. Interestingly, natural ripening and freezing reduced galloylated flavan-3-ol monomers to a greater extent than non-galloylated ones. This study provides new information regarding the susceptibility of flavan-3-ol monomers to freezing and heating, and also suggests that freezing can advance the maturation the seeds of under-ripe red vinifera grapes.

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)0.001
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 score0.787
Threshold uncertainty score0.568

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.016
GPT teacher head0.237
Teacher spread0.222 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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