A comprehensive profiling of phenolic compounds and antioxidant activities of 24 varieties of red raspberry cultivated in Northeast China
Bibliographic record
Abstract
Red raspberries, valued for their nutrients and bioactive compounds, have broad uses in processing and healthy products. However, limited comprehensive research focused on the comparison of phenolic compounds of red raspberry, especially species cultivated in Northeast China, has been reported. This study aimed to conduct a thorough investigation of 24 red raspberry varieties in Northeast China for the first time, evaluating their phenolic compounds and antioxidant capacities. The results showed that 'DNS1' had the highest total phenolic content (TPC), 'Willamette' had the highest total flavonoid content (TFC), and 'Boyne' had the highest total anthocyanin content (TAC). Phenolic compounds in red raspberries were predominantly found in esterified form, while glycosylated phenolics should not be overlooked. Chlorogenic acid, cryptochlorogenic acid, ellagic acid, and arbutin were the main phenolic compounds, and the distribution of their contents varied between varieties. The antioxidant activity in the red raspberry had a close association with the content of phenolic compounds. Principal component analysis (PCA) showed that phenolic compounds and antioxidant activities were higher in samples from 'DNS1', 'Boyne', 'Beijing10', 'DNS5', and 'Willamette' varieties. These varieties should be given priority in breeding programs that aim to boost the utility and bioactive profile of red raspberries. PRACTICAL APPLICATION: Red raspberry is becoming a desirable commercially grown fruit species and is viewed as a new functional food. In this context, this research offers strong support for confirming the quality of 24 varieties of red raspberry and plays a critical role in the food industry. It also indicates the potential sources of superior varieties of red raspberry, which are advantageous for growers and consumers in search of high-quality red raspberry varieties.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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 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".