Amino Acid Analysis Protocols. Catherine Cooper, Nicolle Packer, and Keith Williams, eds. Totowa, NJ: Humana Press, 2001, 265 pp., $84.50. ISBN 0-89603-656-1.
Bibliographic record
Abstract
Amino Acid Analysis Protocols is volume 159 in the Methods in Molecular Biology series published by Humana Press. This is a major collection of analytical techniques devoted to the analysis of amino acids in biotechnology, food analysis, and biomedical research, as well as the amino acid content of patient samples in clinical laboratories. The book contains 18 chapters, each of them describing a specific method for measuring amino acid composition. The majority of the protocols focus on proteins and protein hydrolysates, but there are sections devoted to the analysis of body fluids and physiological samples. In 1963, Moore and Stein developed the original standard method for analyzing amino acids, for which they were awarded the Nobel Prize in 1972. Several dedicated commercial amino acid analyzers have been in existence that use this original procedure in which amino acids are separated by ion-exchange chromatography followed by postcolumn derivatization with ninhydrin and detection by ultraviolet-visible spectrophotometry. Chapter 2 of the book is a protocol that describes an up-to-date version of this original procedure. It also details some of the modifications and improvements that have been made to this method, which is still considered the gold standard against which others are judged. Many of the contributing authors refer to this reference method when describing the numerous improvements, modifications, and specialized applications that have followed the original procedure published almost four decades ago.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.049 | 0.113 |
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".