Recent developments in solid-phase microextraction
Bibliographic record
Abstract
The main objective of this review is to describe the recent developments in solid-phase microextraction technology in food, environmental and bioanalytical chemistry applications. We briefly introduce the historical perspective on the very early work associated with the development of theoretical principles of SPME, but particular emphasis is placed on the more recent developments in the area of automation, high-throughput analysis, SPME method optimization approaches and construction of new SPME devices and their applications. The area of SPME automation for both GC and LC applications is particularly addressed in this review, as the most recent developments in this field have allowed the use of this technology for high-throughput applications. The development of new autosamplers with SPME compatibility and new-generation metal fibre assemblies has enhanced sample throughput for SPME-GC applications, the latter being attributed to the possibility of using the same fibre for several hundred extraction/injection cycles. For LC applications, high-throughput analysis (>1,000 samples per day) can be achieved for the first time with a multi-SPME autosampler which uses multi-well plate technology and allows SPME sample preparation of up to 96 samples in parallel. The development and evolution of new SPME devices such as needle trap, thin-film microextraction and cold-fibre headspace SPME have offered significant improvements in performance characteristics compared with the conventional fibre-SPME arrangement.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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".