Polyphenolic Components and Composition Differences of Fruit Polyphenols in Apple Landraces Grown in Cold Regions
Bibliographic record
Abstract
The composition and content of polyphenols in fruit pericarp and pulp of 12 Apple landraces grown of Heilongjiang Province were determined by ultra-high performance liquid chromatography (UPLC), and the differences in the composition and content characteristics of polyphenols in different varieties were studied, and the main factor analysis and cluster analysis were carried out. The results showed that 5 kinds of 20 kinds of polyphenols were detected in 12 Apple landraces , the main phenolic substances in pericarp were epicatechin and catechin, and the main phenolic substances in pulp were chlorogenic acid, catechin and procyanidin. There were significant differences in the composition and content of polyphenols among different varieties, the smallest coefficient of variation was quercetin 3-glucoside (37.48%), and the largest coefficient of variation was rutin (267.18%). It was found that the content of proanthocyanidins in pericarp and pulp was higher by principal component analysis. Based on the content of polyphenol components, it was found that the results of pulp clustering were more consistent with the field phenotypic identification results and kinship. The results provide data support and theoretical basis for processing and utilization of phenolic substances and breeding of local varieties in Heilongjiang province.
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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".