Variations in the sugars and antioxidant compounds related to root colour in tunisian carrot (daucus carota subsp. sativus) landraces
Bibliographic record
Abstract
Carrot (Daucus carota L.) is the most widely consumed root vegetable since it is an important source of nutritional compounds, mainly antioxidants and sugars. In Tunisia, despite the genetic diversity observed in carrot germplasm, including landraces and wild relatives, no research has been conducted on the biochemical composition of carrot. Thus, this study aims to analyse carotenoids, soluble sugars, total phenols, total flavonoids and colour properties of 14 carrot landraces, in order to determine the diversity among them and evaluate the relationships among their biochemical contents. The main carotenoids identified were a-carotene, B-carotene and lutein. Orange carrots were richer in B-carotene and a-carotene than yellow carrots. The major sugars were sucrose, glucose, fructose and galactose. Significant differences were observed among the Tunisian carrot landraces with respect to their biochemical composition and colour characteristics. Total carotenoids and total sugars ranged from 155.74 to 511.44 ug/g of dw and from 368.77 to 546.79 mg/g of dw, respectively. Total phenols and total flavonoids varied from 24.13 to 41.39 mg GAE/100 g of dw and from 16.51 to 24.85 ug CE/100 g of dw, respectively. Significant, positive and negative correlations were found among the measured parameters. A principal component analysis (PCA) and agglomerative hierarchical clustering (AHC) were performed to classify the Tunisian carrot landraces on the basis of colour properties and biochemical compounds. The PCA divided the landraces into four main groups and AHC classified them into two major clusters. The Tunisian carrot landraces were found to be rich in bioactive compounds; they could be good candidates for future breeding programs.
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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".