Long TE PRESS and STEAM for measuring the triglyceride glycerol CH<sub>2</sub> protons at 3 T
Bibliographic record
Abstract
The glycerol methylene proton resonances (4–4.5 parts per million, ppm), which arise from the triglyceride backbone, are relevant to fat composition assessment and can be measured with proton MRS. The purpose of the presented work is to determine long TE (echo time) point resolved spectroscopy (PRESS) and stimulated echo acquisition mode (STEAM) values at 3 T to resolve the glycerol resonances from that of overlapping water. The response of the glycerol methylene protons of nine edible oils as a function of PRESS and STEAM TE (mixing time, TM = 20 ms) was investigated. In addition, high resolution NMR spectra of the oils were acquired at 16.5 T. Long TE values where J‐coupling losses were lowest were selected, namely a TE of 180 ms for PRESS (first echo time 17 ms) and a TE of 100 ms for STEAM (mixing time 20 ms). Oil olefinic (≈5.4 ppm) to glycerol ratios were calculated from the long TE spectra and correlated with 16.5 T ratios. The two techniques yielded olefinic/glycerol ratios that correlated with 16.5 T ratios (R2 = 0.79 for PRESS and 0.90 for STEAM). The efficacy of the sequences in resolving the glycerol resonance from that of water was verified in vivo on tibial bone marrow of four healthy volunteers. In addition, the potential for using the glycerol methylene signal normalized to the methyl signal (≈0.9 ppm) to assess changes in free fatty acid content was demonstrated by measuring differences in spectra acquired from a triglyceride peanut oil phantom and from a phantom composed of a mixture of peanut oil and free fatty acid oleic acid.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".