One-class modeling for verification of botanical identity: A review

Authors: Harnly, James

Submitted to: FRONTIERS IN PHARMACOLOGY
Publication Type: Review Article
Publication Acceptance Date: 2/19/2025
Publication Date: 3/27/2025
Citation: Harnly, J.M. 2025. One-class modeling for verification of botanical identity: A review. Frontiers in Pharmacology. 16:1-17. https://doi.org/10.3389/fphar.2025.1504230.
DOI: https://doi.org/10.3389/fphar.2025.1504230

Interpretive Summary: Authentication of botanical materials and supplements is of considerable interest. The large commercial market for supplements has led to considerable adulteration and substitution. AOAC International, an organization dedicated to validation of analytical methods, has established guidelines for botanical identification methods. These guidelines, as currently published, have proven cumbersome due to the requirement of specifying an authentic and an adulterated material ( a two-class method) and requiring analysis of 30 samples of each to establish a suitable level of statistical confidence. This study proposes reviews the use of a one-class modeling approach which allows use of simpler chemometric methods. This proposed modeling approach will permit faster and easier detection of adulterated materials and supplements.

Technical Abstract: One-class modeling is a supervised multivariate botanical identification method based on principal component analysis (PCA) that constructs a model based only on the characteristics of a single class of reference samples and uses the Q statistic as a combined metric. Test samples are judged to be similar (authentic) if their combined metric falls within the model limits or different (adulterated or contaminated) if the metric falls outside the model limits. This review initially considers the 3 major factors affecting authentication: the number of variables (univariate versus multivariate), the number of classes (one-class versus multi-class), and the type of analysis (numerical versus non-numerical). Multivariate analysis is commonly used for authentication, providing a broader coverage of the identity specifications of the samples. With a combined metric, multivariate methods are analogous to univariate methods. One-class modeling and two-(or more)-class modeling employ different approaches for authentication with one-class modeling being more suitable to authentication. While most methods to date have had a numerical (quantitative) basis, non-numerical (qualitative) methods are possible. This review focuses on multivariate, one-class modeling based on PCA. Examples are presented for the application of one-class modeling to authentication of Maca (Lepidium meyenii), American ginseng (Panax quinquefolius), Echinacea purpurea, and Black Cohosh (Actaea racemosa). These examples demonstrate the utility of one-class modeling.