Total Phospholipids in Edible Oils by In-Vial Solvent Extraction Coupled with FTIR Analysis
Bibliographic record
Abstract
A simple procedure for the determination of total phospholipids (TPL) in edible oils was developed by combining a single-step, in situ methanol/acetonitrile (MeOH/ACN) extraction of the oil sample followed by Fourier transform infrared (FTIR) spectroscopic analysis of the extract. Spectral analysis of extracts in a 25 μm CaF 2 cell obtained using 1:1 MeOH/ACN added to oil in a 2:1 ratio indicated that measurements made using only the asymmetric phosphate diester PO 2 – stretching band at 1243 cm –1 in second-derivative spectra were sufficient for the accurate measurement of TPL with minimal coextracted triglyceride interferences being encountered. FTIR calibration spectra were devised using only phosphatidylcholine (PC) as a representative phospholipid standard, covering a range of 0–50000 μg/g TPL and spiked into 1:1 MeOH/ACN, capable of tracking the added PC with an SD of <200 μg/g. The FTIR method was initially validated using model PC-spiked degummed canola oil and subsequently with commercial crude and refined soy and rapeseed oils as well as a lecithin tablet with the FTIR TPL predictions compared to those of the AOCS Ca 12-55 molybdenate method. The FTIR method tracked the AOCS results well, being somewhat more reproducible than the reference method (±3.2 vs ±4.9%), which limited its accuracy relative to the AOCS reference procedure (±2.2%). The simple in-vial solvent extraction procedure, followed by FTIR analysis of the extract, is a simple, efficient, and rapid procedure that is also amenable to automation using an autosampler-equipped FTIR if multiple samples are to be analyzed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".