In vivo extraction of volatile organic compounds (VOCs) from Micro-Tom tomato flowers with multiple solid phase microextraction (SPME) fibers
Bibliographic record
Abstract
The in vivo headspace extraction of volatile organic compounds from Micro-Tom tomato flowers was investigated using multiple solid phase microextraction (SPME) fibers of different properties to maximize the extraction selectivity for a nontargeted analysis. The three fibers used in this work were polydimethylsiloxane (PDMS), PDMS/divinylbenzene (DVB), and carboxen (CAR)/PDMS. Two sources for tomato flowers were used: Micro-Tom wild type (WT) and transgenic Micro-Tom overexpressing the carotenoid cleavage deoxygenase 1 gene. Gas chromatography–mass spectrometry (GC–MS) results demonstrated that the largest amounts of volatile organic compounds (VOCs) were observed with the PDMS/DVB fiber for both wild type and transgenic plants, but the CAR/PDMS and PDMS fibers contributed to the detection of selective compounds. Data revealed the presence of 45 VOCs from transgenic plants and 35 from the wild type when all three fibers were used together. Of the total VOCs identified, 30 were common to both types of plants, but 15 were specific to the transgenic and 5 to the wild type plants. The compounds identified from Micro-Tom flowers were mainly monocyclic and bicyclic monoterpenes and sesquiterpenes, with one alkyl benzene compound. The bicyclic monoterpenes, (1R)-α-pinene, (1S)-α-pinene, and β-pinene, were found to be the most abundant molecules present in both wild type and transgenic plants. The overall advantage of maximizing the discovery of VOCs based on the selectivity differences with three SPME fibers was evident. Such a benefit is important in the nontargeted analysis of transgenic plants for detecting the production of unexpected compounds.
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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 | 0.000 | 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) | 0.001 | 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 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".