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Record W2091393356 · doi:10.3390/molecules20035112

Functional Characterization of Individual- and Mixed-Burgundian Saccharomyces cerevisiae Isolates for Fermentation of Pinot Noir

2015· article· en· W2091393356 on OpenAlexafffund
Emily Terrell, Margaret A. Cliff, Hennie Van Vuuren

Bibliographic record

VenueMolecules · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFermentation and Sensory Analysis
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFermentationFood scienceSaccharomyces cerevisiaeEthyl acetateChemistryBiologyYeastOrganic chemistryBiochemistry

Abstract

fetched live from OpenAlex

Pinot noir has traditionally been fermented by native flora of multiple yeasts producing a complex combination of aromas and flavors. With the use of industrial dry yeasts, winemakers gained enological reliability and consistency in their wines, but lost diversity and complexity. This research evaluated the use of co-culturing yeasts to fulfill this dual role. Fermentations of Burgundian Saccharomyces cerevisiae isolates and their mixtures were evaluated for their enological characteristics and production of volatile compounds, at 22 °C and 27 °C. The novel isolates were genetically unique and enologically equivalent to the industrial strains. Analysis of variance and principal component analysis of 25 headspace volatiles revealed differences among the yeasts and between the fermentation temperatures. Wines from the mixed-Burgundian isolates were most similar to one another and could be differentiated from the industrial strains at both 22 °C and 27 °C. Mixed-Burgundian wines at both temperatures had higher concentrations of ethyl esters and acetate esters, compared to the industrial strains which had higher concentrations of higher alcohols at 27 °C and higher concentration of other ethyl esters at 22 °C. Given the unique profiles of the co-cultured wines, this research offers winemakers a strategy for producing wines with unique and more complex characters without the risk of spontaneous fermentations.

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 score0.782
Threshold uncertainty score0.121

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 head0.043
GPT teacher head0.236
Teacher spread0.194 · 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

Citations5
Published2015
Admission routes2
Has abstractyes

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