Analysis of macrolide antibiotics, using liquid chromatography‐mass spectrometry, in food, biological and environmental matrices
Bibliographic record
Abstract
Macrolides are a group of antibiotics that have been widely used in human medical and veterinary practices. Analysis of macrolides and related compounds in food, biological, and environmental matrices continue to be the focus of scientists for the reasons of food safety, pharmacokinetic studies, and environmental concerns. This article presents an overview on the primary biological properties of macrolides and their associated analytical issues, including extraction, liquid chromatography-mass spectrometry (LC-MS), method validation, and measurement uncertainty. The main techniques that have been used to extract macrolides from various matrices are solid-phase extraction and liquid-liquid extraction. Conventional liquid chromatography (LC) with C18 columns plays a dominant role for the determination of macrolides, whereas ultra-performance liquid chromatography (UPLC) along with sub-2 microm particle C18 columns reduces run time and improves sensitivity. Mass spectrometry (MS), serving as a universal detection technique, has replaced ultraviolet (UV), fluorometric, and electrochemical detection for multi-macrolide analysis. The triple-quadrupole (QqQ), quadrupole ion trap (QIT), triple-quadrupole linear ion trap, time-of-flight (TOF), and quadrupole time-of-flight (QqTOF) mass spectrometers are current choices for the determination of macrolides, including quantification, confirmation, identification of their degradation products or metabolites, and structural elucidation. LC or UPLC coupled to a triple-quadrupole mass spectrometer operated in the multiple-reaction monitoring (MRM) mode (LC/MS/MS) is the first choice for quantification. UPLC-TOF or UPLC-QqTOF has been recognized as an emerging technique for accurate mass measurement and unequivocal identification of macrolides and their related compounds.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".