Determination of quantitative nutritional labeling compositional data of lipids by Nuclear Magnetic Resonance (NMR) spectroscopy
Bibliographic record
Abstract
The application of Nuclear Magnetic Resonance (NMR) spectroscopy in the determination of nutrition labeling component data (NLCD) was investigated, with the intent of using this methodology as a primary method to calibrate FTIR instrumentation for NLCD confirmation or screening on a routine basis. Unlike previous NMR studies, this work used three strategies to attain accuracy and reproducibility of NLCD through: (i) appropriate setting of operational parameters for spectral acquisition; (ii) resonance selection by optimizing the signal in proportion to the nuclei population and (iii) integration of resonances by pre-defined fixed chemical shift ranges. Both of 13C NMR spectra and 1H NMR spectra were shown to provide robust and acceptable results on the condition of appropriate acquisition of spectra for quantization purposes and the adoption of standard procedures for spectral processing, integration and calculation purposes. A quantitative approach of NLCD including trans content was determined by the interpretation resonance signals of 13C's and 1H's from methylene groups presented in triglyceride complex of fats and oils. An alternative method based on partial-least-squares (PLS) calibrations was provided as well, the latter proved to be especially useful in dealing with overlapping bands frequently found in 1H spectra. With the diagnostic provided by PLS, the trans and cis signals were shown to be separated in 1H spectra. It is the premise for the trans fat determination based on 1H spectra. Unit conversion from mole to weight % was addressed and a solution was developed based on NMR data per se, without significant assumptions. Validation involving the analysis of three different lipid types (model triacylglycerols, refined and hydrogenated oils) demonstrated that NMR predictions of NLCD were in good agreement with those results either from samples' actual values as well as those obtained using GC and FTIR predictions. Thus with appropriate integration of instrumentation, software and spectral processing accessories, both 13C and 1H NMR can determine NLCD, but with the capability to determine trans, 1H NMR is more practical than 13C NMR due to its much shorter spectral acquisition time. Thus NMR can serve as a primary method for the calibration of FTIR instrumentation, a practical instrumental method for routine NLCD determination and screening.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".