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Record W2804182341 · doi:10.5539/jfr.v7n4p43

Study of the Chemical Components, Bioactivity and Antifungal Properties of the Coffee Husk

2018· article· en· W2804182341 on OpenAlexvenueno aff
Luigi Castaldo, Giulia Graziani, Anna Gaspari, Luana Izzo, Carlos Luz, Jordí Mañes, Mario Rubino, Giuseppe Meca, Alberto Ritieni

Bibliographic record

VenueJournal of Food Research · 2018
Typearticle
Languageen
FieldMedicine
TopicCoffee research and impacts
Canadian institutionsnot available
FundersGeneralitat ValencianaMinisterio de Economía y CompetitividadUniversità degli Studi di Napoli Federico II
KeywordsHuskFood scienceChemistryDPPHAspergillus nigerPolyphenolABTSIngredientAspergillus flavusTrolox equivalent antioxidant capacityAntioxidantBotanyBiologyBiochemistry

Abstract

fetched live from OpenAlex

Coffee husk is a fibrous mucilaginous material representing the major residues from the handling and processing of coffee. Currently, coffee husk is discarded which contributes to environmental pollution. Dry and semi-dry coffee husk were extracted by hot infusion and the polyphenol profile was studied by UHPLC and Q-Orbitrap mass spectrometry. Free radical scavenging activity in the infusion was between 26.61 at 31.33 mmol Trolox equivalent antioxidant capacity (TEAC)/kg, as measured by the assay with ABTS and DPPH radical. The results indicated that coffee husk contained 367.45 and 396.04 g/kg of total dietary fiber in dry and semi-dry sample, respectively. While the infusions contain exclusively soluble dietary fiber 0.64 and 0.98 g/L. The methanol extracts of coffee husk presented antimicrobial activity of P. camemberti, P. expansum and P. roqueforti, A. flavus and A. niger, many of their mycotoxins producers. The minimum inhibitory concentration (MIC) and minimum fungicidal concentration (MFC) values for the Penicilliums strains ranged from 6.3 to 50 g/L, whereas on the Aspergillus strains the data detected ranged from 25 to 50 g/L, respectively. This study indicates the possible exploiting of this coffee by-product as potential natural health promoting and preservative ingredient.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.253
Threshold uncertainty score0.390

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.261
GPT teacher head0.408
Teacher spread0.147 · 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

Citations27
Published2018
Admission routes1
Has abstractyes

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