Can strawberries be made healthier? How breeding can be adapted to increase carotenoid content in strawberries
Bibliographic record
Abstract
Strawberries are an economically and nutritionally important fruit. Their health benefits include being rich in antioxidants which help prevent disease. Carotenoids are a type of antioxidant that have been poorly studied so far in strawberries. I used data collected by researchers at Agriculture and Agri-Food Canada in Kentville to examine variation in carotenoid content across 215 diverse strawberries. Strawberries were grown in Kentville, NS and their carotenoid concentrations were measured using UPLC. The carotenoids measured were antheraxanthin, alpha-Carotene, beta-Carotene, beta-Cryptoxanthin, lutein, neoxanthin, phytoene cluster, violaxanthin, and zeaxanthin. I also used these values to calculate total carotenoid concentration. In addition, several agronomically important traits such as harvest date, fruit weight, and berry colour, were measured. I examined correlations between carotenoids as well as between carotenoids and agronomical traits across these diverse strawberry accessions. Among the 91 pairwise correlations, I identified ten as significant. There were six significant carotenoid-carotenoid correlations and four significant correlations between carotenoids and agronomic traits. The carotenoid-carotenoid correlations tell us that if we breed for high levels of one carotenoid, we may increase the concentration of another carotenoid as well. The agronomic trait correlations tell us valuable information like if we increase berry weight, we are decreasing carotenoid concentration. It is important to understand these correlations when deciding if nutritional value outweighs consumer preferred traits. Ultimately, I find that lutein is likely the most important and promising carotenoid in strawberries due to its presence in 214 of the 215 strawberries evaluated. Future work may include determining the underlying causes of carotenoid abundance variation in strawberries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".