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Record W2059401085 · doi:10.1024/0300-9831/a000146

Rapid Determination of Coenzyme Q10 in Food Supplements Using <sup>1</sup>H NMR Spectroscopy

2013· article· en· W2059401085 on OpenAlexfundno aff
Yulia B. Monakhova, Ingrid Ruge, Thomas Kuballa, Christiane Lerch, Dirk W. Lachenmeier

Bibliographic record

VenueInternational Journal for Vitamin and Nutrition Research · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCoenzyme Q10 studies and effects
Canadian institutionsnot available
FundersMinistry of Rural Affairs
KeywordsChemistryCoenzyme Q10Detection limitChromatographyChloroformNuclear magnetic resonance spectroscopyAnalytical Chemistry (journal)StereochemistryBiochemistry

Abstract

fetched live from OpenAlex

A methodology utilizing 1H NMR spectroscopy has been developed to measure the concentration of coenzyme Q10 (CoQ10) in dietary supplements. For sample preparation, a very simple dilution with deuterated chloroform and addition of internal standard is sufficient. CoQ10 produces a distinct peak of the CH groups in the isoprene side chain of the molecule in the δ 5.15 - 5.05 ppm range, where it can be distinguished from other matrix compounds. The method was shown to be of adequate sensitivity with a limit of detection (LOD) of 7.8 mg/L, to control the CoQ10 content in the majority of the products. The precision expressed as relative standard deviation was around 5 %; linearity was observed from 14 to 2000 mg/L (R = 0.99). The developed methodology was applied for the analysis of 21 food supplements (capsules, tablets, and liquid products). On the basis of the labeled amounts, only two products contained substantially lower concentrations of CoQ10 (57 % and 51 %). All other concentrations varied between 83 % and 190 % with respect to labeling. The developed NMR method may be used by quality assurance laboratories for routine control of CoQ10 products.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.339

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.043
GPT teacher head0.384
Teacher spread0.341 · 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

Citations13
Published2013
Admission routes1
Has abstractyes

Explore more

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