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Record W2803393532 · doi:10.5539/jfr.v7n4p23

Changes in Phenol Level and Antioxidant Activity of Cocoa Beans During Fermentation and Roasting

2018· article· en· W2803393532 on OpenAlexvenueno aff
St. Sabahannur, Suraedah Alimuddin, Rahmawati Rahmawati

Bibliographic record

VenueJournal of Food Research · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Chemistry and Fat Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsFermentationRoastingFood scienceCOCOA BEANPolyphenolDPPHChemistryAntioxidantFlavonoidCatechinPhenolsPhenolCoffee beanBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Flavonoid, polyphenols, especially catechin and epicatechin,are major components in cocoa products, which are known for antioxidant properties. Cocoa bean requires fermentation process in order to obtain good taste. During the fermentation process, polyphenols are oxidized by polyphenol oxidase to form quinone and 2-quinon. The purpose of the research is to determine the total level of polyphenol and antioxidant activity of fermented and roasted cocoa beans. The experiment was using Completely Randomized Design (CRD) with fermentation treatment that includes: Without fermentation, three-day fermentation and five-day fermentation. Observation parameters include: Polyphenol level and antioxidant activity with DPPH method. The results showed that the total phenol level of cocoa bean changed during fermentation and after roasting. The highest phenol level was found in cocoa beans without fermentation, and there is a decrease of phenol level to 98% after fermentation and roasting. The fermentation affects the Inhibition Concentration (IC50) of cocoa beans. An unfermented bean showed a very strongly active antioxidant activity with IC50 of 7.848 ppm, whereas three-day fermentation showed a strong antioxidant activity with IC50 of 35.961 ppm, and five-day fermentation is moderately active with IC50 of 55.976 ppm.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.418
Threshold uncertainty score0.085

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 studies0.0000.000
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 head0.134
GPT teacher head0.339
Teacher spread0.205 · 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

Citations8
Published2018
Admission routes1
Has abstractyes

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