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Record W2612874710 · doi:10.5539/jfr.v6n3p116

Identification and Quantification of Natural Lecithin Phospholipids and Their Residuals in Freeze-Dried and Drum-Dried Fruits and Vegetables by LC-MS and HPLC-ELSD

2017· article· en· W2612874710 on OpenAlexvenueno aff
Xiaoyan Xia, Tiffany Gallegos-Peretz, Boris Nemzer

Bibliographic record

VenueJournal of Food Research · 2017
Typearticle
Languageen
FieldMedicine
TopicPhytochemicals and Antioxidant Activities
Canadian institutionsnot available
Fundersnot available
KeywordsChromatographyChromatography detectorChemistrySunflower oilLecithinPhosphatidylcholineHigh-performance liquid chromatographyMass spectrometryPhosphatidylethanolamineElectrosprayFood sciencePhospholipidBiochemistryMembrane

Abstract

fetched live from OpenAlex

Sunflower lecithin is commonly used as a food processing agent. In this study, residues of sunflower lecithin phospholipids in drum-dried fruits and vegetables were investigated. The contents of phosphatidylcholine and phosphatidylethanolamine were of interest due to their natural levels in fresh fruits and vegetables as well as their residues after the drum drying process. Identification of these compounds in freeze-dried and drum-dried fruits and vegetables was conducted by normal-phase and reverse-phase ultra-high-performance liquid chromatography (UPLC) coupled with Q Exactive Orbitrap electrospray mass spectrometry. Quantification of phosphatidylcholine in various fruits and vegetables was performed using normal-phase high-performance liquid chromatography with evaporative light scattering detector (ELSD). The quantification results from these various products demonstrate that use of de-oiled sunflower lecithin as a processing agent in the drum drying production process does not affect the quality of final drum-dried 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 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)0.001
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
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.055
GPT teacher head0.360
Teacher spread0.305 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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