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.
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 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 | 0.001 | 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".