Which Polyphenolic Compounds Contribute to the Total Antioxidant Activities of Apple?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The antioxidant activities of eight apple cultivars were studied by using the ferric reducing/antioxidant power (FRAP), the beta-carotene-linoleic acid model system (beta-CLAMS), and the photochemiluminescent (PCL) assays. The antioxidant activity of apples is highly correlated to the total phenolic content (TPC) measured by the Folin-Ciocalteu method and the total polyphenolic index (TPI) obtained by HPLC. Extracts of the peel and flesh were analyzed and assayed separately. The FRAP activities of both peel and flesh extracts correlate well with the TPC (r = 0.95 and 0.99, respectively) and the TPI (r = 0.82 and 0.99, respectively). Similar results were found in the beta-CLAMS activities, showing correlation coefficients of r = 0.90 and 0.91 with the TPC for the peel and flesh extracts and of r = 0.90 and 0.84 with the TPI for the peel and flesh extracts, respectively. The antioxidant activity measured by the PCL assay was not correlated with TPC or TPI due to the lack of integratable lag phase in this method with the flavan-3-ols/procyanidins. Among the five major polyphenolic groups, flavan-3-ols/procyanidins had the highest positive correlation with the FRAP and beta-CLAMS activities: r = 0.84 and 0.88 for the peel extracts, respectively; and r = 0.98 and 0.87 for the flesh extracts, respectively. At individual compound level, epicatechin and procyanidin B2 were the major contributors to the antioxidant activity of apple. Hydroxycinnamic acids may have a significant role in the flesh.
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.
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 it