Past, Present, and Future of Solid Phase Extraction: A Review
Bibliographic record
Abstract
Sample preparation of target compounds from biological, pharmaceutical, environmental, and food matrices is one of the most time-consuming steps in analytical procedures. Extraction techniques are dominant, especially those based on the processes running on the phase such as liquid-liquid and/or liquid-solid. Due to the reproducibility of data, precision, relatively low cost of the appropriate analysis, simplicity of the determination, and the possibility of direct combination of those techniques with other methods (both on-line and off-line), they have become the most widespread in routine determinations. Additionally, sample pretreatment procedures have to be more selective, cheap, quick, and environmentally friendly. Selectivity is obtained by using procedures based on immunoaffinity or molecular imprinting. Reduction of costs can be obtained by automation of the extraction procedures (automated SPE) or techniques like column-switching. Reducing at least the time for sample preparation is achieved by introducing miniaturization techniques (multi-well SPE). This review summarizes the current achievements and application of solid phase extraction (SPE). The main aim is to deal with the utilization of different types of sorbents for solid-phase extraction and emphasize the use of new synthesized sorbents as well as to bring together studies on a systematic approach to SPE method development.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.003 |
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".