Characterization of Volatile Flavor Compounds in Supercritical Fluid Separated and Identified in Gurum (Citrulluslanatus Var. colocynthoide) Seed Oil Using HSME and GC–MS
Bibliographic record
Abstract
extraction (SFE) and the screw press process (SPP). For volatile compounds extraction and identification, headspace solid-phase micro-extraction (HS-SPME) and GC-MS were used, respectively. A total number of 56 volatile compounds were revealed and identified in oil extracted by SFE, while only 40 compounds were detected in extracted oil by SPP. Acids, aldehydes, esters, ketones, furans, and other components were present in the highest ratio in oil extracted by SFE. In contrast, alcohols and alkenes were found in the highest proportion in oil extracted by SPP. In this study, it was observed that SFE showed an increase in the amounts of volatile compounds and favorably impacted the aroma of gurum seed oil. The results reveal that different extraction methods significantly impact the volatile components of gurum seed oil, and this study can help evaluate the quality of the oil extracted from gurum seeds.
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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) | 0.001 | 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".