Evaluation of a performic acid oxidation method for quantifying amino acids in freshwater species
Bibliographic record
Abstract
Abstract Thiol amino acids in proteins store metals like mercury, but established methods for their quantitation in freshwater species have had limited application and evaluation. As such, literature on the amino acid composition of aquatic species often lacks the thiols cysteine and methionine. Here, we evaluated a performic acid (PFA) oxidation method to determine its suitability to measure cysteine and methionine, as well as 15 other amino acids, in novel matrices (zooplankton, benthic invertebrates, fishes, and algae). Protein‐bound amino acids were oxidized with PFA, hydrolyzed in hydrochloric acid, derivatized with AccQ·Tag ultra (Waters), and separated by ultra‐high performance liquid chromatography with fluorescence detection. PFA oxidation was successful in determining precise results for 15 amino acids, including the sulfur amino acids, with the complete loss of tyrosine (TYR) and poor precision of phenylalanine (PHE). The overall variability of the method was 11% (excluding TYR and PHE), or 6% when reported as relative percentages, comparable to methods without PFA oxidation in other matrices. Except for TYR and thiols, PFA oxidation did not affect the amino acid composition of these biota. Overall, the findings were reproducible and comparable to other approaches and suggest this is a rigorous method for measuring sulfur amino acids and the overall amino acid composition for aquatic biota, both of which are rare in the literature.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".