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Record W4380422045 · doi:10.1021/acs.jafc.3c01357

Aromatic Yeasts: Interactions and Implications in Coffee Fermentation Aroma Profiles

2023· article· en· W4380422045 on OpenAlexaff
Xin Hui Chin, Sherilyn Ho, Geraldine Chan, Nurhidayah Basri, Megan Teo, Aaron Thong, Falicia Goh, N.D. Lindley, Eric Charles Peterson

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2023
Typearticle
Languageen
FieldMedicine
TopicCoffee research and impacts
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsFermentationAromaLactococcus lactisFood scienceYeastFermentation starterStarterFlavorLactococcusLactic acidBiologyBacteriaFermentation in food processingChemistryBiochemistry

Abstract

fetched live from OpenAlex

Nontraditional yeasts prevalent in tropical agricultural fermentations such as coffee and cocoa are known to contribute to aroma profiles, yet the functional roles and interactions between the associated microbial consortia in a farm fermentation are unclear. Here, boiled green bean extract (GBE) from green coffee beans was developed as a rich screening medium to deconstruct the microbial consortia and their interactions during the fermentation of dried green coffee beans. When cultivated in coculture with S. cerevisiae on GBE, strain-specific groupings with distinct volatile organic profiles were observed for nontraditional yeasts (e.g., Hanseniaspora spp., Pichia kudriavzevii ). Further changes are evident when constructed consortia composed of nontraditional yeast, S. cerevisiae, and Lactococcus lactis var. cremoris were cultured in GBE, and a comparison with abiotically acidified GBE suggests that pH plays a major role in the influence of lactic acid bacteria (LAB) on fermentation aromas. This approach represents a tool for the development of starter culture formulations to create different flavor profiles in coffee fermentation.

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 score0.962
Threshold uncertainty score0.136

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 head0.026
GPT teacher head0.305
Teacher spread0.279 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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