Enzymatic Decarboxylation of Tyrosine and Phenylalanine To Enhance Volatility for High-Precision Isotopic Analysis
Bibliographic record
Abstract
We present a rapid and selective method to increase the volatility of tyrosine and phenylalanine without adding derivative C for high-precision gas chromatography-continuous-flow isotope ratio mass spectrometry (GCC-IRMS) based on enzymatic decarboxylation to yield alkylamines and evaluated for 15N isotopic integrity. Purified tyrosine and phenylalanine were converted to tyramine and phenethylamine by tyrosine and phenylalanine decarboxylases, respectively. GC separation was achieved using a thick stationary phase (5-microm) capillary column. Recoveries were 95 +/- 2%. The reproducibility of delta15N of tyramine and phenethylamine measured by GCC-IRMS averaged SD(delta15N) = 0.33 per thousand. The absolute differences between delta15N of amino acids measured by elemental analyzer-IRMS and the alkylamines measured by GCC-IRMS was not significant. Phenethylamine and tyramine prepared from a mixture of 18 amino acids were extracted by ethanol with 95% recovery, and analysis yielded clean chromatograms and equivalent precision. These data indicate that enzymatic decarboxylation of phenylalanine and tyrosine is a convenient method to increase their volatility for continuous-flow isotopic analysis without introducing extraneous C or significant isotopic fractionation.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".