Automated FTIR Analysis of Free Fatty Acids or Moisture in Edible Oils
Bibliographic record
Abstract
An FTIR spectrometer coupled to an autosampler and attendant methodologies for high-volume automated quantitative analysis of free fatty acids (FFA) or moisture in edible oils are described. Samples are prepared by adding 20 g of oil to a 50 ml screw-capped vial, to which is added either a methanol/NaHNCN solution or dry acetonitrile in a I:I (w/v) ratio for FFA or H2O analysis, respectively. After capping with Mylar-lined septum caps, the vials are loaded into an autosampler tray, which is then agitated vigorously to extract the constituent of interest from the oil into the oil-immiscible solvent, and are then left to stand for ∼ 10 min to allow for phase separation. The upper solvent layer in each vial is aspirated successively into the IR cell, with the Mylar seal allowing facile autosampler needle penetration into the vials. The spectra of the sample extraction solvents serve as spectral backgrounds in addition to being used in monitoring cell path length and verifying cell loading. FFA and H2O analyses are carried out using 100 and 500 μm CaF2 cells, respectively. For FFA analysis, quantification is achieved using the ν (COO) band at 1573 cm−1, while moisture is determined using water absorption bands at 3629 or 1631 cm−1, depending on the moisture range of the samples. Calibration procedures and data are presented. The spectrometer and autosampler are controlled using proprietary Universal Method Platform for InfraRed Evaluation software, which provides a simple user interface and automates the spectral analysis; the output data can also be sent to a Laboratory Information Management System. Validation and performance data obtained with this automated system demonstrate that it is capable of analyzing >60 samples/h, a rate commensurate with the throughput required by commercial contract or high-volume process control laboratories.
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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) | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".