ArticleJournal of AOAC International2023
Botanical Authentication Using One-Class Modeling.
Article in Journal of AOAC International, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Multi-Platform Metabolomics for Fraud Detection in Spices and Botanical Food Supplements: From Fingerprint Acquisition to Decision-Grade Evidence.Molecules (Basel, Switzerland) · 2026Review
- Cultivar to chemotype: characterizing complex botanicals with mass spectrometry metabolomics.Natural product reports · 2026Review
- One-class modeling for verification of botanical identity: a review.Frontiers in pharmacology · 2025Review
Corrections and comments
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Authors and funding
1 author.
Funding
Abstract
backgroundAuthentication methods are necessary to guarantee the integrity of botanical supplements and their ingredients. In 2012, AOAC International published "Guidelines for Validation of Botanical Identification Methods" however these guidelines proved rather cumbersome.
objectiveDevelop a simpler method for validatation based on one-class modeling that only considers authentic materials.
methodsOne-class modeling uses chemometric analysis based on soft independent modeling of class analogy and the specific pre-processing steps of sample vector normalization and autoscaling.
resultsAny unknown sample can be judged authentic or adulterated based on its agreement with the profile of the authentic samples. The sensitivity and accuracy of one-class modeling is improved using sample vector normalization and autoscaling. The limit of detection for any variable is statistically predictable.
conclusionOne-class modeling offers a simple approach to authentication and is applicable to any non-targeted analytical method. Only the characteristics of the authentic samples are necessary and no specification of an adulterant is necessary. HIGHLIGHTS: One-class modeling offers a simple approach to authentication and is easily implemented using any chemometrics platform.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.