Quantification of character‐impacting compounds in <i>Ocimum basilicum</i> and 'Pesto alla Genovese' with selected ion flow tube mass spectrometry
Bibliographic record
Abstract
Basil (Ocimum basilicum) is an important flavourant plant which constitutes the major ingredient of the pasta sauce 'Pesto alla Genovese'. The characteristic smell of basil stems mainly from a handful of terpenoids (methyl cinnamate, eucalyptol, linalool and estragole), the concentration of which varies according to basil cultivars. The simple and rapid analysis of the terpenoid constituents of basil would be useful as a means to optimise harvesting times and to act as a quality control process for basil-containing foodstuffs. Classical analytical techniques such as gas chromatography/mass spectrometry (GC/MS) are, however, slow, technically demanding and therefore less suitable for routine analysis. A new chemical ionisation technique which allows real-time quantification of traces gases, Selected Ion Flow Tube Mass Spectrometry (SIFT-MS), was therefore utilised to determine its usefulness for the assay of terpenoid concentrations in basil and pesto sauce headspace. Trace gas analysis was performed using the NO(+) precursor ion which minimised interference from other compounds. Character-impacting compound concentration was measured in basil headspace with good reproducibility and statistically significant differences were observed between cultivars. Quantification of linalool in pesto sauce headspace proved more difficult due to the presence of interfering compounds. This was resolved by careful selection of reaction product ions which allowed us to detect differences between various commercial brands of pesto. We conclude that SIFT-MS may be a valid tool for the fast and reproducible analysis of flavourant terpenoids in basil and basil-derived foodstuffs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.000 | 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".