Automated analysis of synthesized oligorutin and oligoesculin by laccase
Bibliographic record
Abstract
The polymerization of phenolic species is an interesting way to generate derivatives with new or improved properties. The structural analysis of these compounds is complex and requires specific analytical techniques. The size exclusion chromatography coupled with an UV detector (SEC-UV) and matrix-assisted laser desorption ionization coupled with a time-of-flight detector (MALDI-TOF) analyses are two methods used to investigate the masses of biopolymers. To analyse the oligomers of rutin and esculin synthesized by the laccase from Trametes versicolor , these two techniques were used and coupled with an automated platform. The conditions of detection by MALDI-TOF were optimized. The parallel synthesis, the preparation of sample, the SEC analysis, and the MALDI spotting were realized automatically on a Chemspeed platform. The MALDI-TOF technique enables the detection of oligomers up to pentamer and nonamer, whereas the SEC-UV permits the detection of about 17-mer for rutin and esculin. The molecular mass distribution is underestimated by MALDI-TOF analysis compared with SEC-UV, but comes to the determination of absolute masses. So these two techniques would be combined to explore the masses and the oligomer distributions.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".