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Record W2085716912 · doi:10.1080/14786410802278285

Monitoring the enzymatic degradation of sinigrin from<i>B. juncea</i>meal using<sup>1</sup>H NMR spectroscopy

2009· article· en· W2085716912 on OpenAlexaff
Kody A. Belliveau, Laura Romero‐Zerón

Bibliographic record

VenueNatural Product Research · 2009
Typearticle
Languageen
FieldMedicine
TopicPhytochemicals and Antioxidant Activities
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsSinigrinBrassicaNuclear magnetic resonance spectroscopyChemistrySpectroscopyGlucosinolateDegradation (telecommunications)ChromatographyTwo-dimensional nuclear magnetic resonance spectroscopyBotanyBiologyStereochemistry

Abstract

fetched live from OpenAlex

A simple procedure for extracting and purifying sinigrin from Oriental mustard (Brassica juncea) meal using cold water was developed. Subsequently, the chemical degradation of sinigrin due to enzymatic activity was monitored using (1)H NMR spectroscopy over the course of 30 h. The experimental findings show that sinigrin has been positively identified from B. juncea using (1)H NMR spectroscopy and the prescribed preparation procedure. The results that have been presented also offer unambiguous evidence that (1)H NMR spectroscopy can be used to detect changes in sinigrin concentration over time.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.081
GPT teacher head0.393
Teacher spread0.312 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2009
Admission routes1
Has abstractyes

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