Analysis of edible oils by Fourier transform near-infrared spectroscopy
Bibliographic record
Abstract
Fourier transform near-infrared (FT-NIR) spectroscopy was investigated as a means of quantitative analysis of edible fats and oils. Initially, a method of simultaneously determining the cis and trans content, iodine value and saponification number of neat fats and oils using a heated transmission flow cell was developed. Two partial least squares (PLS) calibrations were devised, a process-specific calibration based on hydrogenated soybean oil and a more generalized calibration based on many oil types, the latter able to analyze oils from a variety of sources accurately and reproducibly. Methodology for the quantitative determination of the peroxide value (PV) of edible oils using a novel glass-vial sample handling system was subsequently developed, based on the stoichiometric reaction of triphenylphosphine with hydroperoxides to form triphenylphosphine oxide. The PV calibration was derived using PLS regression, and the results of a validation study demonstrated that PV could be quantitated accurately if a normalization routine was used to compensate for the inherent dimensional variability of the vials. The vial sample handling system was then used in the development of PLS IV calibrations for the process analysis of commercial oil samples, and these samples were also used to evaluate a global IV calibration devised by Bomem Inc. The discriminant features available through PLS were shown to enhance the accuracy of the IV predictions by facilitating the selection of the most appropriate calibrations based on the spectral characteristics of closely related oils. The predictions obtained using the global IV calibration provided clear evidence that a generalized calibration based on a large and varied selection of oils could provide a means of IV determination by FT-NIR spectroscopy. Subsequently, a generalized FT-NIR trans calibration was developed and shown to yield trans values that were in good agreement with those obtained by the AOCS mid-FTIR single-bounce hori
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 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; both teacher heads agree on what is shown here.
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".