Antioxidant Phytochemicals in Hazelnut Kernel (<i>Corylus avellana</i>L.) and Hazelnut Byproducts
Why this work is in the frame
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Bibliographic record
Abstract
Antioxidant efficacies of ethanol extracts of defatted raw hazelnut kernel and hazelnut byproducts (skin, hard shell, green leafy cover, and tree leaf) were evaluated by monitoring total antioxidant activity (TAA) and free-radical scavenging activity tests [hydrogen peroxide, superoxide radical, and 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical], together with antioxidant activity in a beta-carotene-linoleate model system, inhibition of oxidation of human low-density lipoprotein (LDL) cholesterol, and inhibition of strand breaking of supercoiled deoxyribonucleic acid (DNA). In addition, yield, content of phenolics, and phenolic acid profiles (free and esterified fractions) were also examined. Generally, extracts of hazelnut byproducts (skin, hard shell, green leafy cover, and tree leaf) exhibited stronger activities than hazelnut kernel at all concentrations tested. Hazelnut extracts examined showed different antioxidative efficacies, expected to be related to the presence of phenolic compounds. Among samples, extracts of hazelnut skin, in general, showed superior antioxidative efficacy and higher phenolic content as compared to other extracts. Five phenolic acids (gallic acid, caffeic acid, p-coumaric acid, ferulic acid, and sinapic acid) were tentatively identified and quantified (both free and esterified forms). Extracts contained different levels of phenolic acids. These results suggest that hazelnut byproducts could potentially be considered as an excellent and readily available source of natural antioxidants.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it