Chemical Diversity in Breadfruit (Artocarpus sp.) Inflorescences: Extraction Optimization and GC-MS Analysis
Bibliographic record
Abstract
Throughout tropical regions of the globe there is increasing value observed in Breadfruit (Artocarpus spp.) products from large-scale tree planting projects to gluten-free flour. One of the most interesting anecdotal uses is of male breadfruit inflorescences to repel insects, however little is known about the chemistry of these tissues. The objective of this study was to determine the volatile chemical diversity in extracts of Artocarpus altilis male inflorescences, with a focus on optimizing for extraction conditions that would be amendable to for use in developing tropical regions. Phytochemical profiles were compared for several parameters including extraction solvent composition, technique, temperature and duration. The extraction solvents evaluated included methanol, ethanol, acetone and pentane, over 1,2,5 and 7 days of extraction. Ethanol showed the most diverse chemical profile and was further investigated using multiple extraction methods: soxhlet, reflux and shaking. GC-MS data was compared for all of these extractions, revealing a diverse range of compounds including numerous different terpenoids, sesquiterpenes, triterpenes, fatty acids, alkanes, alcohols and esters. One of the most prevalent classes observed was a group of sesquiterpene alcohols, which have been shown to be biologically active.
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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 | 0.001 |
| 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".