Growing Environment and Nutrient Availability Affect the Content of Some Phenolic Compounds in <i>Echinacea purpurea</i> and <i>Echinacea angustifolia</i>
Bibliographic record
Abstract
Medicinal plant production is different from other agricultural production systems in that the plants are grown for the production of specific phytochemical(s) for human use. To address this need, a Good Manufacturing Practice (GMP)-compliant, controlled-environment production system was developed for production of Echinacea purpurea and Echinacea angustifolia. Within the prototype facility, the growing systems, nutrient availability, water and physical environment were highly controlled. The current study was designed to evaluate the effects of different hydroponic systems, nutrient solution NO (3)(-)/NH (4)(+) ratios and mild water stress on the content of some phenolic compounds in Echinacea plants. The deep-flow solution culture system in which the plant roots were continuously immersed in the nutrient solutions was optimum for the growth of E. purpurea. Higher concentrations of caftaric acid, cynarin and echinacoside were produced in E. angustifolia plants grown in the soil-based growing media while the plants grown in the deep-flow solution system had higher levels of cichoric acid. Altering the NO (3)(-)/NH (4)(+) ratio or limited water stress did not have any significant effect on the phytochemical content of Echinacea plants. Echinacea plants grown in the controlled environment systems had higher or similar amounts of cynarin, caftaric acid, echinacoside and cichoric acid as previously reported in the literature for both field-cultivated and wild-harvested Echinacea plants. This growing system offers the advantages of year-round crop production with minimal contamination by environmental pollutants and common microbes.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".