Comparison of Nutritional and Nutraceutical Properties of Burdock Roots Cultivated in Fengxian and Peixian of China
Bibliographic record
Abstract
This study aimed to analyze and compare the nutritional quality of powders of burdock root from Fengxian (FX) and Peixian (PX) in China. The nutrient composition including carbohydrates, protein, amino acids, vitamin C, carotenoids, as well as total phenols, total flavonoids and phenolic compounds were investigated in addition to in vitro antioxidant capacity. The results showed that the basic nutrients of burdock root powder (BRP) in both locations did not have significant differences (p > 0.05), although the in vitro antioxidant capacity of BRP of Fengxian (F-BRP) was greater than that of PX (p < 0.05). The burdock root peel powder (BRPP) possessed more phenolics and stronger in vitro antioxidant capacity than the burdock root powder (BRP) and peeled burdock root powder (PBRP) (p < 0.05). Moreover, better quality burdock root was obtained from FX. F-BRP was consequently analyzed by ultra-performance liquid chromatography tandem mass spectrometry for its phenolic composition. Seventeen phenolics, mainly caffeoylquinic acids, were detected. In addition, a total of 181 volatile compounds belonging to eight types were detected including alcohols, aldehydes, ketones, alkenes, esters, acids, linear or aromatic hydrocarbons, and others. The diverse compounds found in this study can provide a theoretical basis for the development and utilization of burdock in the food industry.
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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 | 0.001 |
| 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".