Hydrothermal extraction, a promising method for concentrating phenolic antioxidants from red osier dogwood (Cornus stolonifer) leaves and stems
Bibliographic record
Abstract
Red osier dogwood (ROD) ( Cornus stolonifer ) is a popular ornamental shrub in most parts of North America. It has a record of ethnopharmacological uses by native North Americans. With increasing awareness about the health benefits of natural antioxidants, efforts are needed to develop methods for producing plant-based antioxidants as sources of nutraceuticals or functional food ingredients. This study aimed at establishing an optimum temperature for hydrothermal extraction of phenolic compounds from the leaves and stems of ROD. Spray-dried extracts obtained from hydrothermal extraction at four different temperatures, as well as their raw materials and spent residue were analyzed for moisture content and water activity. The samples were extracted with organic solvent and their total phenolic content, phenolic composition and antioxidant activity were also determined. Moisture content was below 10% and the water activity was below 0.6 inclusive, which are recommended for storing dry plant products. Glucogallic acid, ellagic acid, rutin, quercetin 3- O -malonylglucoside and quercetin were the phenolic compounds identified in all the samples. Rutin was the predominant compound. As expected, all the spray-dried extracts had higher phenolic content and antioxidant activity than the raw materials and spent materials. Among the temperatures studied, 98 °C was the most effective in extracting the phenolic compounds. The spray-dried extracts may find application in high-value antioxidant-rich products such as functional food ingredients and nutraceuticals. The spent materials retained a considerable amount of phenolic antioxidants and can therefore be useful in preparing antioxidant-rich animal feed.
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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) | 0.001 | 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) | 0.001 | 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".