Effect of High Pressure Pasteurization on Bacterial Load and Bioactivity of <i>Echinacea Purpurea</i>
Bibliographic record
Abstract
UNLABELLED: High hydrostatic pressure (HHP) technology was applied to organic Echinacea purpurea (E. purpurea) roots and flowers to determine the feasibility of using this technology for cold herb pasteurization, to produce microbiologically safe and shelf-stable products for the natural health products (NHPs) industry. HHP significantly (P < 0.01) reduced microbial contamination in both roots and flowers without affecting the phytochemical retention of chicoric and chlorogenic acids, and total alkamide contents. The antioxidant activity of E. purpurea methanol-derived extracts, evaluated in both chemical (2,2'-azino-bis(3-ethylbenzthiazoline-6-sulphonic acid) [ABTS] and oxygen radical absorption capacity [ORAC] assay) and in cell culture models (RAW264, 7 macrophage, H(2)O(2)-induced intracellular oxidation, and lipopolysaccharide [LPS]-induced nitric oxide production), was not adversely affected by the application of HHP at both 2 and 5 min at 600 mPa. Furthermore, HHP did not affect the capacity of E. purpurea extracts to suppress nitric oxide production in LPS-activated macrophage cells. Therefore, our results show that HHP is an effective pasteurization process treatment to reduce microbial-contamination load while not adversely altering chemical and bioactive function of active constituents present in organic E. purpurea. PRACTICAL APPLICATION: Our study reports for the first time, the effectiveness of using high hydrostatic pressure (HHP) technology pressure to pasteurize E. purpurea root and flower, and the comparative retention of bioactive phytochemicals. Therefore, this technique can be used in food and natural health product industries to produce high-quality, microbiologically safe, and shelf-stable products.
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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 | 0.000 |
| 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".