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Record W2950623536

Simple quantification of phytosterols and tocopherols using fast chromatography -Tandem mass spectrometry.

2018· article· en· W2950623536 on OpenAlexaboutno aff
Alice Demelenne, George Gachumi, Asmita Poudel, Randy W. Purves, Zafer Dallal Bashi, Anas El‐Aneed

Bibliographic record

VenueORBi (University of Liège) · 2018
Typearticle
Languageen
FieldMedicine
TopicCholesterol and Lipid Metabolism
Canadian institutionsnot available
Fundersnot available
KeywordsChromatographyChemistryMass spectrometryTandem mass spectrometrySimple (philosophy)Liquid chromatography–mass spectrometry
DOInot available

Abstract

fetched live from OpenAlex

Tocopherols and Phytosterols are highly abundant compounds in waste products resulting from Canola Oil production. They have significant antioxidant and cholesterol lowering properties, respectively. Since Canola is a major crop product in Canada, effective extraction of these metabolites has economical impact. Hence, there is a need for the development of a fast and easy quantification method of these active metabolites. Our analytical strategy relied on the use of fast chromatography -Tandem mass spectrometry (FC-MS/MS). A guard column was used to achieve fast separation and the method is compared to use of conventional C18 column. The mobile phase consisted of acetonitrile:methanol (99:1) with 0.1% acetic acid. The compounds were ionized in the positive ion mode using atmospheric pressure chemical ionization (APCI). The following parameters were employed: source temperature 380°C, curtain gas 40 psi, nebulizer current 2.5 µA and ion source gas 1 30 psi. 5α-Cholestan-3β-ol and Rac-tocol were used as internal standards for phytosterols and tocopherols, respectively. Four phytosterols and four tocopherols, namely Stigmasterol, β-Sitosterol, Brassicasterol, Campesterol, Alpha-tocopherol, Gamma-tocopherol, Beta-tocopherol and Delta-tocopherol were determined using FC-Multiple-Reaction-Monitoring (MRM). The run time was 2 minutes only, compare to 6.5 minutes with the column injection. Beta-tocopherol and Gamma-tocopherol couldn’t be resolved on the guard column nor on the C18 column. The FC-MS/MS methods addressed the issue of cross talks among the target analytes. For example, stigmasterol and β-sitosterol precursor ions observed as [M+H-H20]+ has the same m/z values for the ion designated as [M+H-4H]+ for campesterol and brassicasterol. In fact, such interferences prevented the full removal of the column (i.e. loop injection). Calibration curves were established and a good linearity was achieved (0.25-10 µg/ml) with R2 of 0.996 and 0.997 for tocopherols and phytosterols, respectively. In conclusion, a fast and simple FC-MS/MS method for the simultaneous quantitation of phytosterols and tocopherols was successfully developed.

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)0.001
metaresearch head score (Gemma)0.002
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: none
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.002

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 head0.024
GPT teacher head0.252
Teacher spread0.229 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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