Standardized Procedure for the Simultaneous Determination of the Matrix Effect, Recovery, Process Efficiency, and Internal Standard Association
Bibliographic record
Abstract
The matrix effects (MEs) on the quantification of an analyte can be significant and should not be neglected during development and validation of an analytical method. According to this premise, we developed a standardized procedure based on a set of six tests performed on six different sample matrices to detect and characterize the effects of the matrix for single and multiple analytes methods. The link between the matrix effect, recovery, process efficiency, accuracy, precision, and calibration curve was underscored by calculations performed with peak areas, ratios of standard/internal standard peak area, and concentrations. The terms instrumental ME and global ME were introduced, and the term recovery was subdivided for clarity. The test accounts for the presence of ubiquitous and endogenous analytes through background subtraction. The results showed the necessity for using samples with an original concentration in the same range and that the concentration selected for the addition had a definite impact on the results. The use of six-sample matrices provided a standard deviation on the results, and this information could be inserted in a method performance result to show precision. The tool also allows for testing of different analytes/internal standard combinations, which helps with the selection of the association with minimum MEs. A UPLC-MS/MS method for the quantification of several phthalate metabolites in urine was developed and validated with this test. This methodology responds to a scientific need for homogeneity, clarity, and understanding of the results and facilitates the decision-making process while lowering the required costs and time.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".