ARS | Publication request: Discrimination among Panax species using spectral fingerprinting

Submitted to: Journal of the Association of Official Analytical Chemists
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: November 1, 2010
Publication Date: March 17, 2011
Citation: Chen, P., Luthria, D.L., Harrington, P.B., Harnly, J.M. 2011. Discrimination among Panax species using spectral fingerprinting. Journal of the Association of Official Analytical Chemists. 94:1411-1421.

Interpretive Summary: Ginseng is one of the most commonly used herbal products in the world. There are three major ginseng species commonly used (Panax quinquefolius L., P. ginseng, and P. notoginseng). The spectral fingerprints, a method that characterizes the substance, of the 3 major ginseng species were acquired using ultraviolet (UV), near-infrared (NIR), and mass spectrometry---three methods to analyze the compounds present. With principal components analysis (PCA), all three methods allowed visual discrimination between all three species. Accurate classification of the three species was obtained using two statistical methods: partial least squares-discriminant analysis (PLS-DA) and fuzzy rule-building expert system (FuRES). Relatively poor accuracy was obtained using abother statistical method, soft independent modeling of class analogy (SIMCA), when a single component was used.

Technical Abstract: Spectral fingerprints of samples of three Panax species (P. quinquefolius L., P. ginseng, and P. notoginseng) were acquired using UV, NIR, and MS spectrometry. With principal components analysis (PCA), all three methods allowed visual discrimination between all three species. All three methods were able to discriminate between white and red ginseng and showed distinctive sub-groupings of red ginseng related to root quality (age/size). Analysis of variance (ANOVA) was used to evaluate the relative variance arising from the species, run, and analytical uncertainty and was used to identify the most information-rich portions of the spectrum for NIR and UV. Accurate classification of the 3 species was obtained using partial least squares-discriminant analysis (PLS-DA) and fuzzy rule-building expert system (FuRES). Relatively poor accuracy was obtained using soft independent modeling of class analogy (SIMCA) when a single component was used.