Characterizing the Phenolic Compounds in Iron Walnut Oil (Juglans sigillata Dode) Across Chinese Regions
Bibliographic record
Abstract
This study examines the chemical composition and antioxidant properties of iron walnut oil (IWO) from different Chinese regions, using ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry for the analysis of phenolic compounds. Regional variations were identified in fatty acid profiles, with elevated α-linolenic acid levels observed in samples from cooler climates (e.g., Liaoning, sample 1) that were 60% higher than in samples from warmer regions (e.g., Sichuan, sample 2). Antioxidant properties, quantified using 1,1-diphenylpicryl phenyl hydrazine (DPPH), 2,2-azinobis-3-ethylbenzothiazoline-6-sulfonate (ABTS), and Ferric ion reducing antioxidant power (FRAP) assays, corresponded to both oil polyphenol content (up to 62.91 mg/kg) and γ-tocopherol concentrations (268.68-525.05 mg/kg). Nineteen phenolic acids and flavonoids were identified, including ellagic acid, gallic acid, p-hydroxybenzoic acid, syringic acid, vanillic acid, quercetin, caffeic acid, ferulic acid, p-coumaric acid, coniferol, and pinoresinol. This comprehensive analysis underscores the nutritional and therapeutic potential of IWO, and delineates the impact of geographic and environmental factors on its quality, providing a scientific foundation for further research and development aimed at enhancing food industry standards and exploring natural product chemistry.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".