Oak Kernels—Volatile Constituents and Coffee-Like Beverages
Bibliographic record
Abstract
Modern consumers are much aware of potential health benefits of food and food ingredients. The food industry has been constrained to develop new products with improved sensory, nutritive and functional characteristics. In this work a potential use of English (Quercus robur) and Turkish oak (Quercus cerris) kernels as functional food components was estimated. Volatiles from native and roasted kernels were isolated using continuous hydro distillation with CH2Cl2 and analyzed with GC/MS. Coffee-like beverages were prepared from roasted kernels of both species and a sensory assessment was conducted. In the native samples the main compounds were beta-eudesmol and palmitic acid (39.9 and 24.9%, respectively) in Q. robur, and palmitic acid (53.8%) in Q. cerris. In the roasted samples the main compounds were furans: furfural (51.7 and 60.6%) and 5-methyl-furfural (8.6 and 9.4%, respectively). Coffee-like beverages from roasted oak samples were evaluated for sensory properties gaining high scores for appearance, with satisfying taste and fullness. The presented results, along with previous findings on substantial antioxidant and antiradical activities of English and Turkish Oak kernels, draw attention to these easy available, cheap, but neglected native raw materials as valuable functional food components. Further investigations on this matter are warranted.
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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) | 0.001 | 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".