A Multi-Pronged Technique for Identifying Equisetum palustre and Equisetum arvense—Combining HPTLC, HPLC-ESI-MS/MS and Optimized DNA Barcoding Techniques
Bibliographic record
Abstract
The most prominent horsetail species, Equisetum arvense, has an array of different medicinal properties, thus the proper authentication and differentiation of the plant from the more toxic Equisetum palustre is important. This study sought to identify different samples of E. arvense and E. palustre using three analytical methods. The first method involved the use of HPTLC analysis, as proposed by the European Pharmacopoeia. The second, HPLC-ESI-MS/MS, is capable of both identification and quantification and was used to determine the Equisetum alkaloid content in each sample. A third method was DNA barcoding, which identifies the samples based on their genetic make-up. Both HPTLC and HPLC-ESI-MS/MS proved to be suitable methods of identification, with HPLC-ESI-MS/MS proving the more sophisticated method for the quantification of alkaloids in the Equisetum samples and for determining the adulteration of E. arvense. For DNA barcoding, optimal primer pairs were elucidated to allow for the combined use of the rbcL and ITS markers to accurately identify each species. As new DNA marker sequences were added to GenBank, the reference library has been enriched for future work with these horsetail species.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".