A New Fourier Transform Infrared Method for the Determination of Moisture in Edible Oils
Bibliographic record
Abstract
A rapid, practical, and accurate Fourier transform infrared (FT-IR) method for the determination of moisture content in edible oils has been developed based on the extraction of water from oil samples into dry acetonitrile. A calibration curve covering a moisture content range of 0-2000 ppm was developed by recording the mid-infrared (MIR) spectra of moisture standards, prepared by gravimetric addition of water to acetonitrile that had been dried over molecular sieves, in a 500 microm ZnSe transmission flow cell and ratioing these spectra against that of the dry acetonitrile. Water was measured in the resulting differential spectra using either the OH stretching (3629 cm(-1) or bending (1631 cm(-1)) bands to produce linear standard curves having standard deviations (SDs) of approximately +/-20 ppm. For moisture analysis in oils, the oil sample was mixed with dry acetonitrile in a 1:1 w/v ratio, and after centrifugation to separate the phases, the spectrum of the upper acetonitrile layer was collected and ratioed against the spectrum of the dry acetonitrile used for extraction. The method was validated by standard addition experiments with samples of various oil types, as well as with oil samples deliberately contaminated with alcohols, hydroperoxides, and free fatty acids to investigate possible interferences from minor constituents that may be present in oils and are potentially extractable into acetonitrile. The results of these experiments confirmed that the moisture content of edible oils can be assessed with high accuracy (on the order of +/-10 ppm) by this method, thus providing an alternative to the conventional, but problematic, Karl Fischer method and facilitating the routine analysis of edible oils for moisture content.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".