HPLTC - A Suitable Tool for Proper Identification of Botanicals? Identification of Licorice Revisited …
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».