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Record W2749861783 · doi:10.1016/j.lwt.2015.12.047

Influence of acetic and lactic acids on cocoa flavan-3-ol degradation through fermentation-like incubations

2016· article· en· W2749861783 on OpenAlexfundno aff
Victor Jos Eyamo Evina, Cédric De Taeye, Nicolas Niemenak, Emmanuel Youmbi, Sonia Collin

Bibliographic record

VenueDIAL (Catholic University of Leuven) · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Chemistry and Fat Analysis
Canadian institutionsnot available
FundersInternational Union of Biochemistry and Molecular Biology
KeywordsFermentationLactic acidFood scienceCOCOA BEANAcetic acidChemistryIncubationCatechinPolyphenolLactic acid fermentationBiochemistryBacteriaBiologyAntioxidant

Abstract

fetched live from OpenAlex

The biochemical reactions occurring inside cocoa beans during fermentation are mostly due to penetration of lactic and acetic acids issued from microbial activities. In the present study, fresh, ripe cocoa beans were subjected to three fermentation-like incubation schemes: incubation for 2 days at pH 4 in a solution of acetic acid, lactic acid, or both, followed by a 3-days incubation at pH 5 in acetic acid solution. After each treatment applied, the fermentation index was above 1 and a cut test revealed a brown color, characteristic of well-fermented beans. As shown by RP-HPLC-ESI(−)-MS/MS analysis, the main flavan-3-ols found in German Cocoa (Amelonado group) and ICS 40 (Trinitario group) ranked as follows: epicatechin > C1 > B2 > catechin > B5 > dehydrodiepicatechin A. In both natural fermentations and fermentation-like incubations, these compounds showed a sharp decrease, this effect being strongest when acetic acid was present from the start. Lactic acid exhibited a somewhat polyphenol-protective effect. Two procyanidins with a molecular weight of 576, undetected before fermentation, were evidenced here for the first time in both incubated and naturally fermented cocoa beans.

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 score0.832
Threshold uncertainty score0.119

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 head0.014
GPT teacher head0.202
Teacher spread0.188 · 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

Citations49
Published2016
Admission routes1
Has abstractyes

Explore more

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