An Automated Headspace SPME-GC-ITMS Technique for Taste and Odour Compound Identification
Bibliographic record
Abstract
Abstract A simple, automated and efficient method is presented for routine analysis of taste and odour compounds in water samples. The following compounds were investigated: hexanal, heptanal, 2t,4t-heptadienal, 2t,4t-octadienal, 2t,6t-nonadienal, 2t,6c-nonadienal, 2-methylisoborneol (MIB), 2t,4t-nonadienal, β-cyclocitral, 2t,4t-decadienal, geosmin and β-ionone. Headspace solid-phase microextraction (SPME) coupled with gas chromatography (GC) and low-resolution ion-trap mass spectrometry (ITMS) was used for quantification of these compounds. A 65-µm polydimethylsiloxane/divinylbenzene (PDMS/DVB) fibre was used for headspace SPME analysis. Method detection limits for all compounds analyzed are at the ng/L level and are lower in most cases than sensory analysis can detect. The method was employed for the analysis of lake and reservoir water samples from several coastal British Columbia drinking water sources during the summer of 2001. Geosmin was the only compound detected and was found at concentrations up to 26 ng/L. To our knowledge the method presented is unique in terms of full automation and represents a sensitive and efficient tool for studying and monitoring taste and odour compounds in water sources.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".