Determining nutritional labeling data for fats and oils by <sup>1</sup>H NMR
Bibliographic record
Abstract
Abstract The nutrition labeling compositional data (NLCD) required for fat‐containing food products consists of the percentages of saturated, cis‐monounsaturated, and cis‐polyunsaturated fat as well as trans content. The capability of 1H NMR spectroscopy to determine the NLCD components in oils that do not contain significant levels of trans isomers has already been established in the literature, but not its capability to differentiate between cis‐ and trans‐unsaturation. In the present study, the determination of all four NLCD components in fats and oils has been demonstrated for the first time. A preliminary analysis of the intensity‐normalized 1H NMR spectra of defined mixtures of pure triacylglycerols (TAG) by partial least squares (PLS) regression revealed that the (mono)allylic proton resonances of cis and trans bonds were sufficiently well separated to allow for accurate quantitation of trans content by simple peak integration. This chemometric approach also served to facilitate the identification of optimal integration limits for these cis‐ and trans‐allylic resonances. Fixed integration limits were also set for the other resonances employed in the determination of the four NLCD components, and a standardized spectral preprocessing procedure was established. The 1H NMR NLCD data obtained for the TAG mixtures by this methodology was a good match to the actual values, calculated from the known molar composition of these gravimetrically prepared mixtures. A procedure for the conversion of the NMR mol% NLCD to units of wt%, previously developed for 13C NMR, was adapted for 1H NMR and shown to be effective in compensating for the overestimation of wt% saturates and underestimation of wt% unsaturates by 1H NMR if this conversion is not made. The 1H NMR methodology for NLCD determination was validated by analyzing AOCS Laboratory Proficiency Program GC samples as well as samples taken from a hydrogenator over time and analyzed for trans content by GC and IR spectroscopy. Comparison of the 1H NMR mol% and wt% NLCD obtained for these validation samples with the data obtained from the reference methods indicated that 1H NMR can deliver high‐quality, accurate NLCD, much like 13C NMR, but in a much shorter time frame. Thus, 1H NMR provides a more rapid and cost‐effective means of obtaining NLCD than 13C NMR and can replace GC as a primary reference method for the calibration of simpler and automatable instrumental methods such as Fourier transform infrared (FTIR) spectroscopy.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".