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Record W3093376082 · doi:10.1021/acsomega.0c03463

Assessment of Melatonin and Its Precursors Content by a HPLC-MS/MS Method from Different Romanian Wines

2020· article· en· W3093376082 on OpenAlexfundno aff
Camelia Albu, Letiția Elena Radu, Gabriel Lucian Radu

Bibliographic record

VenueACS Omega · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFermentation and Sensory Analysis
Canadian institutionsnot available
FundersOntario Ministry of Research, Innovation and Science
KeywordsWinemakingMelatoninHigh-performance liquid chromatographyWineChemistryChromatographyFermentationFood scienceBiology

Abstract

fetched live from OpenAlex

Because melatonin has strong antioxidant activity and wine is an alcoholic beverage of economic relevance, in the present work, the impact of some variable parameters that may occur in the winemaking process on the concentrations of melatonin and its precursors in Romanian wines was studied. Therefore, a sensitive and selective high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) method was developed for the simultaneous analysis of melatonin, serotonin, and l-tryptophan, and some method performance parameters including selectivity, detection limit, precision (by comparing with an alternative HPLC-FL method), accuracy, and robustness were validated. These determinations are significant and the final amounts of analytes are dependent on the microorganisms involved in the winemaking process, the grape variety, geographic regions of vineyards, and aging of wines. In the future, the method may be useful to increase the melatonin content and the antioxidant activity in wines by improved steps in the winemaking process, especially based on application of selected yeasts and improved fermentation conditions.

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 score0.863
Threshold uncertainty score0.266

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 head0.052
GPT teacher head0.276
Teacher spread0.224 · 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

Citations25
Published2020
Admission routes1
Has abstractyes

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