Berry quality and nutritional composition in seabuckthorn ( <i>Hippophae rhamnoides</i> L.): a multi-year evaluation of selectively bred genotypes
Bibliographic record
Abstract
This study explores selective breeding in shaping the yield, berry size, and nutritional quality of seabuckthorn ( Hippophae rhamnoides L.), a nutrient-dense berry crop with significant value-added potential. Utilizing a diverse germplasm collection evaluated over two growing seasons, we identified superior genotypes exhibiting enhanced berry weight, higher Brix values, and improved nutritional profiles. Fifty-berry weights varied from 13.2 to 38.6 g, with genotypes exhibiting notable improvements in size and sugar content. Multivariate analyses, including principal component analysis and hierarchical clustering of metabolic data, revealed distinct year-to-year differences, underscoring the strong influence of environmental conditions on fatty acids and flavonoid composition. Specifically, 2015 showed significant increases in pulp and seed lipid content, omega-3 and omega-6 fatty acids, while saturated fatty acids remained stable. Flavonoid profiling revealed elevated levels of kaempferol and quercetin without changes in total flavonoid content. Conversely, total and individual sugars were significantly higher in 2014. Carotenoid profiling during berry development revealed dynamic changes in lutein, lycopene, and carotene accumulation. Integrating seed nutritional composition with agronomic performance across both seasons, we identified genotypes AAFC-24, AAFC-13, AAFC-5, and AAFC-2 as stable, high-performing genotypes with superior yield and nutritional traits. These findings highlight the combined effects of genetics and environment on seabuckthorn berry quality, offering valuable insights for breeding programs aimed at optimizing yield, nutritional value, and functional food applications.
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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 | 0.001 | 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".