Optimization of Triacylglycerol‐estolide Analysis by Matrix‐Assisted Laser Desorption/Ionization‐Mass Spectrometry
Bibliographic record
Abstract
Abstract Acylglycerols containing more than three acyl groups (TAG‐estolides) have been reported in plant seed oils and oil from ergot fungus. These TAG‐estolides have considerable potential for industrial use, however, costs of producing synthetic TAG‐estolides limits their use in large‐scale applications. Identification and structural characterization of additional natural sources of TAG‐estolides has been restricted by their complexity and limitations of current analytical techniques. In this work, detection and characterization of TAG‐estolides was optimized for use with MALDI‐TOF‐MS. Eight commonly used matrices were compared; 2,5‐dihydroxybenzoic acid (DHB) and 2,4,6‐trihydroxyacetophenone (THAP) gave good quality mass spectra. Matrix additives were examined and lithium was the most suitable, since MS/MS spectra of lithiated TAG‐estolides provided the most informative fragmentation using an optimized method. The matrix solution pH was examined, and for THAP, replacing LiCl with 10–40 mM LiOH resulted in a slightly basic pH and significantly more intense TAG‐estolide signals (up to eightfold higher). Since DHB is acidic, a larger amount of LiOH (>150 mM) was required for the matrix solution to become basic, leading to ion suppression and reduced signal intensities. Thus, for TAG‐estolide analysis, THAP with ~20 to 30 mM LiOH gives the highest quality spectra and the most informative MS/MS fragmentation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".