Phenolic composition of 91 Australian apple varieties: towards understanding their health attributes
Bibliographic record
Abstract
INTRODUCTION: Apples, an important contributor to total dietary phenolic intake, are associated with cardiovascular health benefits. Determining the phenolic composition of apples, their individual variation across varieties, and the phenolic compounds present in plasma after apple consumption is integral to understanding the effects of apple phenolics on cardiovascular health. METHODS: Using liquid chromatography we quantified five important polyphenols and one phenolic acid with potential health benefits: quercetin glycosides, (-)-epicatechin, procyanidin B2, phloridzin, anthocyanins, and chlorogenic acid, in the skin and flesh of 19 apple varieties and 72 breeding selections from the Australian National Apple Breeding program. Furthermore, we measured the phenolic compounds in the plasma of 30 individuals post-consumption of an identified phenolic-rich apple, Cripp's Pink. RESULTS: Considerable variation in concentration of phenolic compounds was found between genotypes: quercetin (mean ± SD: 16.1 ± 5.9, range: 5.8-30.1 mg per 100 g); (-)-epicatechin (mean ± SD: 8.6 ± 5.8, range: 0.2-19.8 mg per 100 g); procyanidin B2 (mean ± SD: 11.5 ± 6.6, range: 0.5-26.5 mg per 100 g); phloridzin (mean ± SD: 1.1 ± 0.6, range: 0.3-4.3 mg per 100 g); anthocyanins (mean ± SD: 1.8 ± 4.4, range: 0-40.8 mg per 100 g); and chlorogenic acid (mean ± SD: 11.3 ± 9.9, range: 0.4-56.0 mg per 100 g). All phenolic compounds except chlorogenic acid were more concentrated in the skin compared with flesh. We observed a significant increase, with wide variation, in 14 phenolic compounds in plasma post-consumption of a phenolic-rich apple. CONCLUSION: This information makes an important contribution to understanding the potential health benefits of apples.
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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 | 0.000 |
| 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 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".