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Record W2346796514 · doi:10.1002/ejlt.201600040

Distribution of glucosinolates in camelina seed fractions by HPLC‐ESI‐MS/MS

2016· article· en· W2346796514 on OpenAlexaff
Deyun Yuan, Youn Young Shim, Jianheng Shen, Pramodkumar D. Jadhav, Venkatesh Meda, Martin J. T. Reaney

Bibliographic record

VenueEuropean Journal of Lipid Science and Technology · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMoringa oleifera research and applications
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCamelina sativaGlucosinolateCamelinaChromatographyChemistryHigh-performance liquid chromatographyElectrospray ionizationMass spectrometryElectrospraySinigrinGlucoraphaninChromatography detectorBrassicaBotanyFood scienceBiology

Abstract

fetched live from OpenAlex

High glucosinolate concentrations were detected and identified in camelina ( Camelina sativa L. Crantz.) seed fractions using reversed phase high performance liquid chromatography‐electrospray ionization‐mass spectrometry (HPLC‐ESI‐MS) in multiple reaction monitoring mode. Total glucosinolate quantitation was performed using proton nuclear magnetic resonance spectrometry using N , N ‐dimethylformamide as an internal standard. Individual glucosinolate concentrations were determined by analysis on extracted ion chromatograms generated by a MS detector. Distribution of glucosinolates in C. sativa seed fractions during pressing is reported. Practical applications: HPLC‐ESI‐MS and HPLC‐ESI‐MS/MS using monolithic HPLC columns were used to detect, quantify, and identify the three major glucosinolates in Camelina sativa L. and fractions of C. sativa produced by processing. Quantitative extracted ion MS chromatograms afforded excellent quantitation of individual glucosinolates. The use of monolithic HPLC columns enabled rapid analysis of these samples. Schematic of a proposed dehulling system for Camelina sativa seed.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.717
Threshold uncertainty score0.229

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.014
GPT teacher head0.234
Teacher spread0.221 · 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

Citations9
Published2016
Admission routes1
Has abstractyes

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