Preparation and application of in-fibre internal standardization solid-phase microextraction
Bibliographic record
Abstract
The in-fibre standardization method is a novel approach that has been developed for field sampling/sample preparation, in which an internal standard is pre-loaded onto a solid-phase microextraction (SPME) fibre for calibration of the extraction of target analytes in field samples. The same method can also be used for in-vial sample analysis. In this study, different techniques to load the standard to a non-porous SPME fibre were investigated. It was found that the appropriateness of the technique depends on the physical properties of the standards that are used for the analysis. Headspace extraction of the standard dissolved in pumping oil works well for volatile compounds. Conversely, headspace extraction of the pure standard is an effective approach for semi-volatile compounds. For compounds with low volatility, a syringe-fibre transfer method and direct extraction of the standard dissolved in a solvent exhibited a good reproducibility (<5% RSD). The main advantage of the approaches investigated in this study is that the standard generation vials can be reused for hundreds of analyses without exhibiting significant loss. Moreover, most of the standard loading processes studied can be performed automatically, which is efficient and precise. Finally, the standard loading technique and in-fibre standardization method were applied to a complex matrix (milk) and the results illustrated that the matrix effect can be effectively compensated for with this approach.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".