Phenolic profiles and their responses to pre- and post-harvest factors in small fruits: a review
Bibliographic record
Abstract
The consumption of small fruits has increased in recent years. Besides their appealing flavor, the commercial success of small fruits has been partially attributed to their high contents of phenolic compounds with multiple health benefits. The phenolic profiles and contents in small fruits vary based on the genetic background, climate, growing conditions, and post-harvest handling techniques. In this review, we critically compare the profiles and contents of phenolics such as anthocyanins, flavonols, flavan-3-ols, and phenolic acids that have been reported in bilberries, blackberries, blueberries, cranberries, black and red currants, raspberries, and strawberries during fruit development and post-harvest storage. This review offers researchers and breeders a general guideline for the improvement of phenolic composition in small fruits while considering the critical factors that affect berry phenolics from cultivation to harvest and to final consumption.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".