New CAMAG HPTLC application note: Authenticity assessment of botanical materials using One-Class Support Vector Machine Classification in VisionCATS

Authenticating botanical materials can be challenging, especially when a large numbers of samples must be evaluated or differences between chromatographic fingerprints are subtle.

CAMAG's latest application note demonstrates how One-Class Support Vector Machine (OC-SVM) classification in visionCATS can be combined with HPTLC fingerprinting to support a standardised, data-driven approach to authenticity assessment, using red Asian ginseng as an example.

Technical summary:

• Uses authenticated samples to establish the reference class

• Does not require examples of every possible adulterant or substitute

• Evaluates complete HPTLC fingerprint profiles

• Flags samples that do not conform to the authenticated reference model