Rapid quantitation of fish oil fatty acids and their ethyl esters by FT‐NIR models
Bibliographic record
Abstract
Abstract Consumption of fish oil and dietary supplements containing eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) has steadily increased because of their reported health benefits. A rapid procedure based on Fourier Transform Near Infrared Spectroscopy (FT‐NIR) models was developed for analysis of fish oil and their ethyl ester derivatives to replace the time consuming GC method. Inclusion of fish oil or ethyl esters containing varied concentrations of OA, EPA, and DHA into the FT‐NIR classification models made possible their classification and quantification. Accurate GC analysis is essential in developing reliable quantitative models since FT‐NIR is matrix dependent. Development of FT‐NIR models based on 30 m PEG capillary GC column results, as recommended by the official GC method for analysis of marine oils, proved problematic, since these columns did not resolve many geometric isomers compared to 100 m highly polar cyanopropyl polysiloxane columns. Depending on the content of geometric isomers in fish oils and ethyl esters, the levels of long‐chain n‐3 PUFA would be overestimated if the model used were based on the results from a 30 m column. The FT‐NIR method was found to be applicable to all fish oil and ethyl ester samples, except when fatty acids were outside the range examined, or contaminants were present. The FT‐NIR method was applicable to analysis of in‐plant intermediates provided contaminants were absent, or identified so they could be incorporated into the model. The FT‐NIR method was suitable to evaluate the shelf life of n‐3 PUFA concentrates.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".