Identification and Quantitation of the Volatile Compounds Responsible for the Aroma of Pawpaw (Asimina triloba)
Bibliographic record
Abstract
The aim of this study was to perform an exhaustive analysis of the aroma active compounds in pawpaw fruit using gas chromatography-olfactometry (GC-O) on capillary GC columns with a variety of extraction techniques such as solid phase microextraction (SPME) and solvent extraction. The volatile extracts were obtained using headspace solid phase micro-extraction (HS-SPME) for 30 min at two temperatures (23°C and 50°C), and by solvent extraction with dichloromethane. The extracts were analyzed using gas chromatography-mass spectrometry (GC-MS) and gas chromatography-olfactometry (GC-O). The SPME extraction at 50°C caused an increase in the levels of Strecker aldehydes (3-methyl butanal, methional, and phenylacetaldehyde). To eliminate potential artifacts, the study focused on SPME at 23°C and solvent extraction as the methods for characterization of the odor-active compounds in pawpaw. Forty-four odor active compounds were detected in the pawpaw fruit, including fifteen compounds that were identified in pawpaw for the first time. Some of the newly reported compounds, with high flavor dilution values include homofuraneol, eugenol, vanillin, acetaldehyde, diacetyl, gamma-octalactone and delta-octalactone. These high intensity odor active compounds, in combination with the many esters, contribute to give the sweet, creamy, mango, pineapple and banana-like character used to describe the flavor of pawpaw fruit. In addition, quantitation of these compounds was achieved in this study. These results provide new understanding into the volatile compounds responsible for the aroma of pawpaw fruit.
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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) | 0.000 | 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 | 0.000 | 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 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".