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Record W2900178306 · doi:10.5539/jas.v10n12p65

Phenolic Compounds and Polyamines in Grape-Derived Beverages

2018· article· en· W2900178306 on OpenAlexvenueno aff
Héctor Alonzo Gómez Gómez, Igor Otávio Minatel, Cristine Vanz Borges, Márcia Ortiz Mayo Marques, Evandro Tadeu da Silva, Gean Charles Monteiro, Marlon Jocimar R. da Silva, Marco Antônio Tecchio, Giuseppina Pace Pereira Lima

Bibliographic record

VenueJournal of Agricultural Science · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPolyamine Metabolism and Applications
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsChemistryFood scienceWinePhenolsVitis viniferaSugarBotanyOrganic chemistryBiology

Abstract

fetched live from OpenAlex

Isotopic analyses and chromatographic analysis (phenolic compounds and biogenic amines), can be applied to investigate the functional and nutritional quality in different grape juices and wines. These beverages when produced exclusively from grapes contain bioactive compounds. In this way, the isotopic analysis, as well as the determination of phenolic compounds and biogenic amines were performed with the aim of verify the functional quality of juices and wines produced with Vitis vinifera and Vitis labrusca grapes. The samples that were analyzed consisted of four whole juices, two nectars, two V. vinifera wines, and two V. labrusca wines. Regarding the isotopic analyses, only one nectar, among the beverages studied, presented the addition of sugar from C4 plants. Wines from V. vinifera showed the highest content of biogenic amines and phenols; whereas, the highest content of anthocyanins were found in V. labrusca. The levels of biogenic amines and phenolic compounds were variable between samples, and recommendations for consumers should be made considering several conditions, such as physiological state, age, consumption, among others. Anthocyanins and biogenic amines, as well as isotopic analyses, can be applied as tools to measure the quality of grape derived beverages.

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 score0.686
Threshold uncertainty score0.146

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 head0.007
GPT teacher head0.247
Teacher spread0.240 · 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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