Novel approaches to automated quality control analyses of edible oils by Fourier transform infrared spectroscopy : determination of free fatty acid and moisture content
Bibliographic record
Abstract
Three new quantitative Fourier transform infrared (FTIR) spectroscopic methods were developed for the analysis of edible oils: two procedures to measure free fatty acids (FFA) and one to measure moisture (H2O), the latter two methods ultimately being automated and implemented on an auto-sampler equipped FTIR spectrometer. The methods developed for FFA determination both convert FFAs to their carboxylate salts by means of acid/base reaction without causing oil saponification, one approach using 1-propanol, an oil-miscible solvent, and the other using methanol, an oil-immiscible solvent into which the FFA salts are extracted. The first method involves splitting oil samples into two halves, with one half treated with propanol containing base and the other half with propanol only. The spectra of each half is collected and a differential spectrum obtained, from which quantization is performed. The methanol procedure simply involves extracting FFA into methanol containing a weak base and quantitating the FFA salts produced. Both FFA methods determine the FFA content by measuring the v (COO-) absorbance at ~1570 cm-1 relative to a reference wavelength of 1820 cm-1 from a differential spectrum relative to the solvent, the extraction procedure being superior in terms of both speed and sensitivity, being able to measure FFA levels down to ~0.001%. The method developed for moisture determination involves extracting water in edible oils into dry acetonitrile and then quantitating it by measuring the absorbance of the OH stretching band (3629 cm-1) and/or the HOH bending band (1631 cm -1). All three methods were validated by standard addition experiments, evaluated for potential interferences, and, in the case of FFA determination, compared to the performance of AOCS official methods. The results indicated that the extraction-based procedures were superior to conventional wet chemical methods in both sensitivity and reproducibility. The FFA and H2O extraction procedures were subsequently automated by connecting an auto-sampler to the FTIR spectrometer and developing procedures and software algorithms to enable the analysis of up to 100 samples/h. The methods developed and implemented are a substantive improvement over conventional methods for the analysis of FFA and H2O in edible oils and provide a means by which QC and process laboratories can analyze large volumes of edible oils for these two important parameters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".