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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".