Metabolic Profiling and Antioxidant Analysis for the Juvenile Red Fading Leaves of Sweetpotato
Bibliographic record
Abstract
Leaves of sweetpotato (Ipomoea batatas L.) are promising healthy leafy vegetable. Juvenile red fading (JRF) leaves of sweetpotato, with anthocyanins in young leaves, are good candidates for developing functional vegetables. Here, metabolic profiling and possible antioxidants were analyzed for five leaf stages of the sweetpotato cultivar “Chuanshan Zi”. The contents of anthocyanins, total phenolics, and flavonoids all declined during leaf maturation, corresponding to declining antioxidant activities. By widely targeted metabolomics, we characterized 449 metabolites belonging to 23 classes. A total of 193 secondary metabolites were identified, including 82 simple phenols, 85 flavonoids, 18 alkaloids, and eight terpenes. Analysis of the metabolic data indicates that the antioxidant capacity of sweetpotato leaves is the combined result of anthocyanins and many other colorless compounds. Increased levels of “chlorogenic acid methyl ester”, a compromised form of chlorogenic acid, significantly correlated with the declined antioxidant abilities. Besides anthocyanins, some significant metabolites contributing to the high antioxidant property of the sweetpotato leaves were highlighted, including chlorogenic acids, isorhamnetin glycosides, trans-4-hydroxycinnamic acid methyl ester, 4-methoxycinnamic acid, esculetin, caffeate, and trigonelline. This study provides metabolic data for the utilization of sweetpotato leaves as food sources, and sheds light on the metabolomic change for JRF leaves of other plants.
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 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 | 0.000 | 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 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".