Analytical Assessment of Nitroimidazoles in Honey Samples from South eastern Albania, utilizing SupelMIP™ SPE Columns with LC/MS-MS
Bibliographic record
Abstract
The global issue of colony collapse disorder (CCD) has led to substantial losses in bee populations, affecting both apiculture and agriculture reliant on bees for pollination.Despite extensive investigations into potential causes, the precise reasons behind honey bee decline, particularly related to nitroimidazoles such as metronidazole (MNZ), dimetridazole (DMZ), ronidazole (RNZ), and ipronidazole (IPZ), remain elusive.This study explores the impact of nitroimidazoles on honey bee health, emphasizing the need for effective analytical methods.The investigation focuses on the utilization of SupelMIP™ SPE columns in conjunction with LC-MS/MS for evaluating nitroimidazoles in honey.The method shows high sensitivity and selectivity, addressing challenges associated with matrix effects.The study includes honey samples collected from the southeastern region of Albania, employing a meticulous sample preparation protocol.The analytical methodology, employing SupelMIP™ SPE columns, demonstrates excellent extraction efficiency, minimal matrix interference, and adherence to regulatory detection limits.The research reveals the presence of nitroimidazoles in certain honey samples, with concentrations exceeding regulatory standards.Chromatograms obtained through LC-MS/MS confirm the reliability of the method.The results emphasize the significance of monitoring nitroimidazole residues in honey to safeguard bee health and ensure the integrity of honey as a consumable product.Furthermore, the study delves into the optimization of mass spectrometer conditions, presenting detailed MS/MS parameters for target analytics.Conclusions highlight the effectiveness of the SupelMIP™ SPE column extraction method, offering advantages over alternative approaches.The research also discusses the broader implications of nitroimidazole residues in honey for human health, underscoring the importance of stringent monitoring practices.In conclusion, the combination of SupelMIP™ SPE columns with LC-MS/MS emerges as a robust and efficient approach for evaluating nitroimidazoles in honey, contributing valuable insights to the ongoing discussions on honey bee health and food safety.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".