Phytochemical Characterization of Five Edible Purple-Reddish Vegetables: Anthocyanins, Flavonoids, and Phenolic Acid Derivatives
Bibliographic record
Abstract
Vegetables comprise a significant portion of our daily diet with their high content in nutrients including fiber, vitamins, minerals, as well as phenolic compounds. Vegetable consumption has been shown to be positively associated with the prevention of several degenerative diseases thanks to their bioactive compounds. Accordingly, five selected vegetables, namely, red chicory, red onion, eggplant, purple sweet potato, and black carrot were thoroughly assessed for their phenolic content in this study. For this purpose, the total phenolic and flavonoid content of these five vegetables and their antioxidant activities that are based on three common methods ABTS radical cation decolorization assay (ABTS), Cupric Ion Reducing Antioxidant Capacity (CUPRAC), and DPPH scavenging activity assay were determined. Additionally, HPLC-PDA/Electrospray ionization coupled with mass spectrometry (HPLC-PDA/-ESI+-MS)-based identification and quantification of the members belonging to polyphenols present in each vegetable were determined. Statistical correlations between antioxidant activities and the specific type of phenolic compounds, such as anthocyanins, flavonoids, anthocyanins, and phenolic acids were further elucidated. Phenolic acids (chlorogenic and syringic acids) were found to be the most abundant compounds that are present in all samples. Among the anthocyanins, cyaniding derivatives were present in all vegetables. In terms of their antioxidant activities, the analyzed vegetables were ranked as red chicory > purple sweet potato > black carrot > eggplant > red onion, in descending order. Superior antioxidant activities exhibited by red chicory and purple sweet potato were attributed to the high content of phenolic compounds, especially flavonols (quercetin-3,4-O-diglucoside) in red chicory and anthocyanins (peonidin-3-caffeoyl p-hydroxybenzoylsophoroside-5-glucoside) in purple sweet potato.
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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".