Screening of Tropical Fruit Volatile Compounds Using Solid-Phase Microextraction (SPME) Fibers and Internally Cooled SPME Fiber
Bibliographic record
Abstract
In this study, the optimization and comparison of an internally cooled fiber [cold fiber with polydimethylsiloxane (PDMS) loading] and several commercial solid-phase microextraction (SPME) fibers for the extraction of volatile compounds from tropical fruits were performed. Automated headspace solid-phase microextraction (HS-SPME) using commercial fibers and an internally cooled SPME fiber device coupled to gas chromatography-mass spectrometry (GC-MS) was used to identify the volatile compounds of five tropical fruits. Pulps of yellow passion fruit (Passiflora edulis), cashew (Anacardium occidentale), tamarind (Tamarindus indica L.), acerola (Malphigia glabra L.), and guava (Psidium guajava L.) were sampled. The extraction conditions were optimized using two experimental designs (full factorial design and Doehlert matrix) to analyze the main and secondary effects. The volatile compounds tentatively identified included alcohols, esters, carbonyl compounds, and terpernes. It was found that the cold fiber was the most appropriate fiber for the purpose of extracting volatile compounds from the five fruit pulps studied.
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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 | 0.001 | 0.001 |
| 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".