<sup>13</sup>C NMR as a primary method for determining saturates, <b><i>cis</i></b>‐ and <b><i>trans</i></b>‐monounsaturates and polyunsaturates in fats and oils for nutritional labeling purposes
Bibliographic record
Abstract
Abstract The suitability of 13 C NMR as a primary method for the analysis of lipids to obtain nutritional labeling compositional data (NLCD), i.e. the percentages of saturated, cis ‐monounsaturated, trans ‐monounsaturated, and cis ‐polyunsaturated fat, was assessed. The 13 C NMR methodology was developed by using mixtures of pure triglycerides as model lipids to optimize and standardize scan conditions and spectral pre‐processing procedures, establish fixed integration limits for measurement of the 13 C resonances used in the determination of NLCD, and evaluate the quantitative accuracy of the 13 C NMR analysis. The standardized 13 C NMR methodology allowed the NLCD of the model triacylglycerol mixtures to be determined within ˜±1%. To further evaluate the methodology, two sets of validation samples, consisting of ten unhydrogenated oils from the American Oil Chemists' Society Laboratory Proficiency Program (AOCS‐LPP) and two trans ‐containing AOCS‐LPP samples combined with three samples from a hydrogenation process, were analyzed. Good overall agreement between the NMR‐determined NLCD (in units of mol‐%) and the mol‐% NLCD calculated for these samples from fatty acid compositional data obtained by gas chromatography was found, including good tracking of the trans content in the second validation set. Given that the NLCD must be expressed on a wt‐% basis to be of practical utility, a means of mol‐% to wt‐% conversion was developed assuming all unsaturates to be C 18 and obtaining the weight‐average molecular weight of the saturated fatty acid contributions from the NMR data. This conversion was shown to be especially effective for oil blends, where errors become significant if unit conversion is not done. This work indicates that 13 C NMR can provide excellent primary NLCD data, even in wt‐% terms, which can be used for calibrating simpler and automatable instrumental methods such as FTIR spectrometers to determine or screen for NLCD for fats and oils or lipids extracted from food on a routine basis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".