The determination of six taste and odour compounds in water using Ambersorb 572 and high resolution mass spectrometry
Bibliographic record
Abstract
A method for the analysis of six taste and odour causing compounds in aqueous samples using the granular adsorbent, Ambersorb 572, and gas chromatography-high resolution mass spectrometry (GC-HRMS) has been developed. This method for the determination of geosmin, 2-methylisoborneol (2-MIB), 2-isopropyl-3-methoxypyrazine (IPMP), 2-isobutyl-3-methoxypyrazine (IBMP), 2,3,6-trichloroanisole (236-TCA) and 2,4,6-trichloroanisole (246-TCA) is highly productive [up to 40 samples per day + 23 quality control (QC) samples] and provides rapid (24-48 h) turnaround times. The analytes are extracted from water by the addition of Ambersorb 572 to the sample bottle and rolling for 1 h. The adsorbent is isolated by filtration and allowed to air dry for 1 h. The Ambersorb 572 is transferred to an autosampler vial and the analytes are desorbed into dichloromethane. The extract is analysed by GC-HRMS at 7000 resolving power (RP). Quantification is performed by isotope dilution and internal standard techniques utilizing d3-geosmin, d3-2-MIB, d5-246-TCA and 2-sec-butyl-3-methoxypyrazine (s-BMP). Method precisions of 3.5-5.8% and accuracies of +/- 5.7-8.9% were obtained. Reporting detection limits (RDLs) of 1.0 ng L-1 were obtained for 2-MIB, geosmin, IPMP and IBMP, while RDLs of 2.0 ng L-1 were obtained for 236-TCA and 246-TCA.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 0.001 |
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".