%0 Journal Article
%T Variation in Botanical Reference Materials: Similarity of Actaea Racemosa Analyzed by Flow Injection Mass Spectrometry.
%A Harnly J
%A Upton R
%J J AOAC Int
%V 107
%N 2
%D 2024 Mar 1
%M 38141206
%F 2.028
%R 10.1093/jaoacint/qsad137
%X <B>BACKGROUND: </B>Botanical reference materials (BRMs) generally account for the species, cultivar, and year and location of harvest that result in variability in the chemical composition that may lead to statistically significant differences using chemometric methods.<BR><B>OBJECTIVE: </B>To compare the chemical composition of five species of Actaea root BRMs, four herbal sources of A. racemosa root BRMs, and A. racemosa BRMS, and commercial roots and supplements using chemometric methods and selected pre-processing approaches.<BR><B>METHODS: </B>Samples were analyzed by flow injection mass spectrometry (FIMS), principal component analysis (PCA), and factorial multivariate analysis of variance (mANOVA).<BR><B>RESULTS: </B>Statistically significant (P = 0.05) compositional differences were found between three genera (Actaea, Panax, and Ginkgo), five species of Actaea (A. racemosa, A. cimicifuga, A. dahurica, A. pachypoda, and A. rubra) root BRMs, four herbal sources of A. racemosa root BRMs, and A. racemosa BRMS and commercial roots and supplements. The variability of 6% of the BRM variables was found to be quantitatively conserved and reduced the compositional differences between the four sources of root BRMs. Compositional overlap of A. racemosa and other Actaea BRMs was influenced by variation in technical repeats, pre-processing methods, selection of variables, and selection of confidence limits. Sensitivity ranged from 94 to 97% and specificity ranged from 21 to 89% for the pre-processing protocols tested.<BR><B>CONCLUSIONS: </B>Environmental, genetic, and chemometric factors can influence discrimination between species and authentic botanical reference materials.<BR><B>CONCLUSIONS: </B>Frequency distribution plots derived from soft independent modeling of class analogy provide excellent means for understanding the impact of experimental factors.