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Record W2952819173 · doi:10.5539/jas.v11n9p134

Characterization of Bioactive Compounds in Northern Amazon Fruits

2019· article· en· W2952819173 on OpenAlexvenueno aff
Ismael Montero-Fernández, Edvan Alves Chagas, Antônio Alves de Melo Filho, Selvin Antonio Saravia Maldonado, Ricardo Carvalho dos Santos, Pedro Rômulo Estevam Ribeiro, Pollyana Cardoso Chagas, Ana Cristina Gonçalves Reis de Melo

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural and Food Sciences
Canadian institutionsnot available
Fundersnot available
KeywordsCarotenoidChemistryPulp (tooth)Vitamin CFood scienceBotanyPigmentVitaminHorticultureBiologyBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Fruits and vegetables are highly appreciated because they are constituted by active phytochemicals with functional properties for the organism acting with modulating pharmacological effect. Given the pharmacological properties of this type of food, in this work were studied the concentrations of vitamin C, total carotenoids and reducing and non-reducing sugars of nine fruits developed in the northern Amazon region: Abiu, Acerola, Araçá, Bacuparí, Biribá, Camu-camu, Fruta-do-conde, Graviola and Tapereba. The concentration of vitamin C, the highest concentration in the shell of Camu-camu 2521.51 mg 100 g-1 and for acerola with 1731.4 mg 100 g-1 stand out. The highest concentrations of total carotenoids were also found for the Camu-camu, with concentrations of 0.67 mg 100 g-1 the shell of Camu-camu and 0.57 mg 100 g-1 for the pulp. The concentrations of sugars are higher for the pulps, with the highest concentrations for the pulp of the Fruta-do-conde with 16.31 g 100 g-1 followed by the pulp of the Graviola, both of the Annonaceae family with a concentration of 15.61 g 100 g-1. The different bioactive molecules were correlated for the different parts of the fruit, by means of multivariate analysis techniques (PCA and HCA), where 90.1% of the cases were explained for the pulps, 65.4% for the shell of the fruits and finally the 88.5% of the cases for the seeds. Given the results obtained in this work, these fruits can be used for the preparation of foods with functional interest.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.826
Threshold uncertainty score0.156

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.013
GPT teacher head0.206
Teacher spread0.193 · 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 designObservational
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

Citations1
Published2019
Admission routes1
Has abstractyes

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