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Record W2162743757 · doi:10.1080/10408398.2010.529193

Application of LC and LC-MS to the Analysis of Melatonin and Serotonin in Edible Plants

2011· review· en· W2162743757 on OpenAlexaff
Xin Huang, Giuseppe Mazza

Bibliographic record

VenueCritical Reviews in Food Science and Nutrition · 2011
Typereview
Languageen
FieldNeuroscience
TopicCircadian rhythm and melatonin
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsMelatoninSerotoninChemistryChromatographyFood scienceBiologyBiochemistryEndocrinology

Abstract

fetched live from OpenAlex

Melatonin is a neurohormone produced by the pineal gland of animals. Serotonin is a monoamine neurotransmitter and one of the precursors of melatonin biosynthesis. These two indoleamines have recently been reported to have widespread occurrence in many edible plants. Consuming foodstuffs containing melatonin and serotonin could raise their physiologic concentrations in blood and enhance human health. Literature concerning analytical methods suitable for determination of melatonin and serotonin in edible plants is limited, although several liquid chromatographic (LC) techniques have been used for their quantification. Liquid chromatography-mass spectrometry (LC-MS) methods combine selectivity, sensitivity, and high precision, and enable the simultaneous determination of melatonin and serotonin. This work reviews LC and LC-MS techniques used to determine melatonin and serotonin, and the available data on melatonin and serotonin levels in edible plants.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.958
Threshold uncertainty score0.594

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0000.001
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 head0.108
GPT teacher head0.380
Teacher spread0.272 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations79
Published2011
Admission routes1
Has abstractyes

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