Dibutyrate derivatization of monoacylglycerols for the resolution of regioisomers of oleic, petroselinic, and <i>cis</i>‐vaccenic acids
Bibliographic record
Abstract
Dibutyrate derivatives of monoacylglycerols of oleic, petroselinic, and cis-vaccenic acids were prepared by diesterification of monoacylglycerols with n-butyryl chloride. The resulting triacylglycerols were analyzed by gas chromatography (GC) with a 65% phenyl methyl silicone capillary column and separated on the basis of both fatty acid composition and regiospecific position. The petroselinic acid derivatives eluted first, followed sequentially by the oleic and cis-vaccenic acid derivatives, with the sn-2 positional isomer eluting before the sn-1 (3) isomer in each case. Separation of the peaks was almost baseline between petroselinic and oleic acids as well as between oleic and cis-vaccenic acids. To assess the accuracy of the method, mixtures of triolein, tripetroselinin, and tri-cis-vaccenin in various known proportions were partially deacylated with the use of ethyl magnesium bromide and derivatized and analyzed as above. The results showed that this method compares favorably to the existing methods for analysis of oleic, petroselinic, and cis-vaccenic fatty acids by GC with respect to peak separation and accuracy, and it also provides information on the regiospecific distribution of the fatty acids. The method was applied to basil (Ocimum basilicum) and coriander (Coriandrum sativum) seed oils. cis-Vaccenic, oleic, and linoleic acids were mainly distributed at the sn-2 position in basil seed oil, and higher proportions of linolenic, palmitic, and stearic acids were distributed at the sn-1(3) position than at the sn-2 position. In coriander seed oil, petroselinic acid was mainly distributed at the sn-1 (3) position, and both oleic and linoleic acids were mostly located at the sn-2 position, whereas palmitic, stearic, and cis-vaccenic acids were located only at the sn-1 (3) position.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; a candidate call from one teacher head, 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".