Electrochemical Determination of Artemisinin in Artemisia annua L Herbal Tea Preparation and Optimization of Tea Making Approach
Bibliographic record
Abstract
가끔 먼 지역 거주자들은 현대 의약품이나 의학 서비스에 있어서 불충분하거나 접근할 수 없다. 그들은 개똥쑥의 선택된 품종을 경작하고 차 제조의 적절한 방법에 따라 식물로부터 차나 달인즙을 만드는 것에 의해 말라리아에 대항한 치료의 관점에서 이익을 얻을 수 있다. 아르테미시닌에 대한 최대 추출 효율을 위해, 개똥쑥의 차제조의 다른 방법들은 발달된 DPP방법을 적용하여 연구되었고 이 논문에 서술되었다. 차는 시간을 다르게 하여 3가지 다른 방법으로 제조된다(굽기, 섞거나 섞지 않으면서 굽지 않기 그리고 마이크로 웨이브 오븐). 결과로부터, 아르테미시닌의 더 높은 농도(84.7%)는 15분 동안 섞으면서 굽지 않는 차 제조법에 의해 도달될 수 있다는 것을 발견했다(R.S.D. 2.34%). 아르테미시닌의 농도는 마이크로 웨이브 오븐에서 1.5분 이상 구울 때 감소한다. 최대한도의 추출(88.9%)은 증류수에서 5%에탄올과 함께 섞는 추출방법에서 가능했다(R.S.D. 2.28%). Sometimes inhabitants in remote areas have inadequate or no access to modern medicines or medical services. They can get benefit in term of the treatment against malaria by cultivating selected breeding of A. annua and making teas or decoctions from the plant materials following the proper way of tea preparation. In order to have the maximum extraction efficiency for artemisinin, different ways of tea preparations of A. annua were investigated by applying the developed DPP method and described in this article. Tea was prepared by three different ways (cooking, without cooking with/without shaking and microwave oven) with different times. From the results, it has been found that higher concentration of artemisinin (84.7%) can be attained by following the approach for tea preparation without cooking with shaking for 15 minutes (R.S.D. 2.34%). The concentration of artemisinin decreases with cooking more than 1.5 min in microwave oven. The utmost extraction (88.9% of artemisinin) is possible to extract by shaking with boiled 5% ethanol in distilled water (R.S.D. 2.28%).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 teacher head, 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".