Analysis of Volatile Organic Compounds in Olive Oil of ‘<i>Koroneiki</i>’ with Different Maturity Indices by GC-IMS
Bibliographic record
Abstract
This study aims to determine the optimal harvest period of olives by distinguishing the olive oils with different fruit maturity indices (MIs). Gas chromatography ion-mobility spectrometry (GC-IMS) technology was employed to qualitatively and differently analyze the volatile organic compounds (VOCs) of olive oil extracted from eight MIs of 'Koroneiki' olive fruits, harvested in Longnan City, Gansu Province, China. The results showed that 40 signal peaks were isolated in the eight olive oils with different MIs, and 33 VOCs were identified. These include alcohols (7 kinds), esters (7 kinds), aldehydes (6 kinds), ketones (5 kinds), acids (2 kinds), olefins (2 kinds), and other compounds (4). A total of 20 differential markers for key flavors, with variable importance in the projection (VIP) > 1, were screened out by orthogonal partial least squares - discriminant analysis (OPLS-DA). The results showed that the olive oil samples of the 7th maturity index (QJ7), QJ8, and QJ5, QJ6 have significant differences from the other four olive oils. This suggests that olive oils with different maturity indices can be effectively distinguished.
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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 | 0.000 | 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) | 0.000 | 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".