Comparison of Natural and Roasted Turkish Tombul Hazelnut (<i>Corylus avellana</i> L.) Volatiles and Flavor by DHA/GC/MS and Descriptive Sensory Analysis
Bibliographic record
Abstract
Natural (raw) and roasted hazelnuts were compared for their differences in volatile components and sensory responses. A total of 79 compounds were detected in both hazelnuts, of which 39 (27 positive, 5 tentative, and 7 unknown) were detected in natural hazelnut and 71 (40 positive, 14 tentative, and 17 unknown) were detected in roasted hazelnut. These included ketones, aldehydes, pyrazines, alcohols, aromatic hydrocarbons, furans, pyrroles, terpenes, and acids. Pyrazines, pyrroles, terpenes, and acids were detected in roasted hazelnut only. Concentrations of several compounds increased as a result of roasting and these may play significant roles in the flavor of roasted hazelnut. Pyrazines together with ketones, aldehydes, furans, and pyrroles may contribute to the characteristic roasted aroma of hazelnut. Descriptive sensory analysis (DSA) showed that some flavor attributes such as "aftertaste", "burnt", "coffee/chocolate-like", "roasty", and "sweet" were rated significantly higher in roasted hazelnut compared to its natural counterpart. Natural and roasted hazelnuts can be distinguished using these attributes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".