HPLTC - A Suitable Tool for Proper Identification of Botanicals? Identification of Licorice Revisited …
Bibliographic record
Abstract
Since the enforcement of cGMP for dietary supplements by the FDA and tightening regulation for botanicals not only in Europe and Canada, news about noncompliant companies and mislabeled or adulterated products spread almost daily. At the same time the scientific literature, conferences and workshops are full of reports about new sophisticated tools and complex approaches that are supposed to be the answer to the fundamental problem: how to identify botanicals properly. Starting out with the pharmaceutical industry proven HPLC for quantitation of markers, nowadays new fingerprint techniques like IR and NMR spectroscopy seem to become embraced by the scientific and analytical communities. Because identity is originally biologically determined more recently genetic investigations and DNA barcoding is getting tremendous attention. At the other end of the spectrum are old fashioned techniques such as microscopy and organoleptic tests. Over the years High Performance TLC has become widely accepted by pharmacopoeias, industry and regulatory agencies as a well suited tool for the identification of botanicals including herbal drugs, powdered herbal material and extracts. The availability of valid methods that are fit for purpose is increasing as more monographs are being published, analysts are beginning to adapt a standardized methodology for HPTLC, and SOPs for development and validation of methods are being followed. Good examples for very successful HPTLC methods are those adopted by the European Pharmacopoeia for the identification of Black cohosh and its adulteration with related species at the 5% level as well as the test for the presence of aristolochic acids at the 5 ppm level in Traditional Chinese Medicines. More than 150 methods for identification have been published by the International Association for the Advancement of HPTLC. Among the first published HPTLC methods of identification that were validated according to the proposal by Reich et al [1] was that for licorice. According to most pharmacopoeias licorice root comes from either one or a mixture of the following three species Glycyrrhiza glabra L., Glycyrrhiza uralensis Fisch, Glycyrrhiza inflata Bat.. Although the first two species seem to dominate the market it is rather difficult to differentiate the species. To be sure an expert has to look at the flowering plant. The original method includes two quite similar types of fingerprints that do not correlate with the species. From a regulatory point this is not critical as long as the material is called licorice. However many current samples on the marked are labeled with a species name and therefore it seems necessary to distinguish those. An improved HPTLC method is presented which allows discrimination of G. glabra. and G. uralensis with certainty. The correlation of the different fingerprints with the species was verified in a blind study by genetic sequence analysis. HPTLC has proven to be a reliable, rapid and comparably simple tool for identification. References: [1] Reich E, et al. (2008)J AOAC Int, 91(1): 13 – 20.
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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 | 0.002 | 0.002 |
| 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 | 0.001 | 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 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".