Mass Spectrometry Based Method Development for Monitoring Degradation of Coffee Beans and Mechanistic Exploration into Methylation Enhancement of Phospholipids Using Diazomethane
Bibliographic record
Abstract
Mass spectrometry is a powerful analytical tool with endless potential for developing new scientific methods and making new discoveries in science.In part I of this work, headspace GCMS was used to monitor and identify volatile chemicals, attributing to the unique flavours in roasted coffee, which diminished as the coffee beans aged.These flavour components were shown to vary in relative abundance randomly over time and the extent to which they varied was seemingly random as well.Though strong conclusions could not be made, comparing the change in peak area over a period of months appears to be an optimistic method to use in order to evaluate the coffee's quality with measurable accuracy.In part II of this work, nanoESI mass spectrometry was used to show mechanistically how diazomethane methylates phospholipids.The methylation of sphingomyelin and phosphatidylethanolamine were shown to undergo complete conversion in different solvent mixtures.PE -Phosphatidylethanolamine PEEK -Polyether ether ketone pKa -(-log10) of the acid dissociation constant, Ka SM -Sphingomyelin Part I -Mass spectrometry based method development for monitoring degradation of coffee beans
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".