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Record W2168529776 · doi:10.5539/jfr.v3n6p179

Enrichment of Commercially-Prepared Juice With Pomegranate (Punica granatum L.) Peel Extract as a Source of Antioxidants

2014· article· en· W2168529776 on OpenAlexaffvenue
Zilmar Meireles Pimenta Barros, Jocelem Mastrodi Salgado, Priscilla Siqueira Melo, Fúvia de Oliveira Biazotto

Bibliographic record

VenueJournal of Food Research · 2014
Typearticle
Languageen
FieldNursing
TopicPomegranate: compositions and health benefits
Canadian institutionsAgriculture and Agri-Food Canada
FundersFundação de Amparo à Pesquisa do Estado de São PauloJohns Hopkins University
KeywordsPunicaFood scienceAntioxidantChemistryPhytic acidVitamin CPolyphenolHealth benefitsTraditional medicineBiochemistryMedicine

Abstract

fetched live from OpenAlex

<p>Ready-to eat foods meet the demands of a modern lifestyle and the number of people seeking food that is convenient and safe is increasing. The extracts of peels from four different fruits were tested as potential value-added foods to offer to consumers. Physical and chemical analyses of the peel extracts were conducted to measure total phenolic compounds, tannins, phytic acid and antioxidant activity using the 1’-1’Diphenyl-2’picrylhydrazyl, and 2,2’-azino-bis-3-ethylbenzothiazoline-6-sulphonic acid methods. The result of screening the antioxidant activity showed that the pomegranate peel had higher activity than the other peels (p<0.05). In addition, flavonoids and vitamin C were measured in the pomegranate peel, and low amounts of these components were found. The pomegranate peel had a high amount of phenolic compounds and high levels of antioxidants, and this peel was used to enrich a commercially-available juice. Furthermore, the sensory evaluation showed no difference between the control and enriched juice. The product was well accepted and feasible from a technological standpoint. Because the waste is rich in bioactive compounds, value is added to the final product, as these antioxidant compounds are known to protect health and improve the quality of life of the consumers.</p>

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)0.004
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 score0.280
Threshold uncertainty score0.617

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.052
GPT teacher head0.378
Teacher spread0.326 · 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

Citations17
Published2014
Admission routes2
Has abstractyes

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