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Record W2568307621 · doi:10.1021/acs.analchem.6b04944

Quantitative Nonaqueous Capillary Electrophoresis–Mass Spectrometry Method for Determining Active Ingredients in Plant Extracts

2017· article· en· W2568307621 on OpenAlex
Jianhui Cheng, Lingyu Wang, David D. Y. Chen

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAnalytical Chemistry · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPhytochemistry and biological activity of medicinal plants
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsChemistryChromatographyCapillary electrophoresisMass spectrometryCapillary electrophoresis–mass spectrometryReproducibilityEmodinElectrolyteAnalytical Chemistry (journal)Electrospray ionization

Abstract

fetched live from OpenAlex

Nonaqueous capillary electrophoresis (NACE) is very well suited for online coupling with mass spectrometry due to the relatively high volatility and low surface tension of most organic solvents. Here we present a quantitative NACE-ESI-MS/MS method for separating and determining physcion, chrysophanol, and aloe-emodin in rhubarb. Dantron was used as an internal standard to ensure accuracy and reproducibility in quantitative analyses. Parameters including the pH, background electrolyte (BGE) composition, flow-through microvial chemical modifier solution composition, and modifier solution flow rate were carefully optimized. The developed method was validated by assessing its precision, LODs, and linear range. The contents of physcion, chrysophanol, and aloe-emodin in rhubarb were determined to be 0.22%, 1.0%, and 0.17%, respectively.

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.

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)0.002
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 score0.083
Threshold uncertainty score0.457

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 science0.0010.000
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 head0.046
GPT teacher head0.315
Teacher spread0.269 · 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