Application of Spectral Accuracy to Improve the Identification of Organic Compounds in Environmental Analysis
Why this work is in the frame
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Bibliographic record
Abstract
Correct identification of a chemical substance in environmental samples based only on accurate mass measurements can be difficult especially for molecules >300 Da. Here is presented the application of spectral accuracy, a tool for the comparison of isotope patterns toward molecular formula generation, as a complementary technique to assist in the identification process of organic micropollutants and their transformation products in surface water. A set of nine common contaminants (five antibiotics, an herbicide, a beta-blocker, an antidepressant, and an antineoplastic) frequently found in surface water were spiked in methanol and surface water extracts at two different concentrations (80 and 300 μg L –1 ). They were then injected into three different mass analyzers (triple quadrupole, quadrupole-time-of-fight, and quadrupole-orbitrap) to study the impact of matrix composition, analyte concentration, and mass resolution on the correct identification of molecular formulas using spectral accuracy. High spectral accuracy and ranking of the correct molecular formula were in many cases compound-specific due principally to conditions affecting signal intensity such as matrix effects and concentration. However, in general, results showed that higher concentrations and higher resolutions favored ranking the correct formula in the top 10. Using spectral accuracy and mass accuracy it was possible to reduce the number of possible molecular formulas for organic compounds of relative high molecular mass (e.g., between 400 and 900 Da) to less than 10 and in some cases, it was possible to unambiguously assign one specific molecular formula to an experimental isotopic pattern. This study confirmed that spectral accuracy can be used as a complementary diagnostic technique to improve confidence levels for the identification of organic contaminants under environmental conditions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it