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Chrysanthemum is a common medicinal and edible Chinese herbal product, and its growth environment affects its quality and market sale price. So it has high practical value and relevance to establish an efficient, rapid and accurate approach to identify the origin of chrysanthemums. In this study, we collected chrysanthemum samples from six geographical indication (GI) production areas in China and analyzed the four stable isotope ratios (δ 13C, δ 15N, δ 18O, and δ 2H) and 44 mineral element signatures of chrysanthemums to classify chrysanthemum. An analysis of variance (ANVOA) indicated that stable isotope ratios and mineral elements differed significantly between origins. The accuracies of the training set and prediction set of the random forest (RF) algorithm were 100% and 98.3%, respectively, while those of the partial least squares discriminant algorithm (PLS-DA) were 96.9% and 96.7%, respectively; the results of the RF algorithm were slightly better than those of the PLS-DA. Combining stable isotopes and mineral elements could enhance the accuracy of origin identification. Moreover, the OPLS-DA (orthogonal partial least squares discriminant analysis) models for stable isotopes and mineral elements indicated that δ 2H, δ 18O, Cd, Zn, Mg, Ca, Tm, As, and Tl were the elements that could most effectively distinguish the origins of chrysanthemum, and the discriminant accuracy reached 100%. Therefore, the proposed strategy can offer a simple, rapid, and dependable approach for classifying the geographic origin of chrysanthemums. • Chrysanthemum samples were collected from 6 GI production areas in China. • Geographic identification of chrysanthemum with stable isotopes and elements is feasible. • Cd, Zn, Mg, Tm, As, Ca, and Tl play an important role in classification. • OPLS-DA significantly enhanced the traceability capability for chrysanthemum products. [ABSTRACT FROM AUTHOR]

Lilii bulbus is an important food in China with medicinal and edible functions, and its quality and price are closely related to its geographical origin. In this work, 4 stable isotopes (δ13C, δ15N, δ2H and δ18O) and 41 mineral elements of lilii bulbus samples from 11 origins and 4 species were collected and combined with chemometrics to identify origins and species. ANOVA and principal component analysis (PCA) showed significant differences (P < 0.05) in stable isotopes and multiple elements in lilii bulbus samples from different origins, and Cu, Zn, K, δ18O, Sr and Li are important contributors to the classification. Partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to classify different species of lilii bulbus with good classification performance, while indicating that K, Ca, Na and Mg elements could be used as effective elements for species classification. The external validation model of OPLS-DA showed high accuracy (>89%) for lilii bulbus species classification. This study shows that stable isotopes and multi-elemental combined chemometrics provide an effective method for lilii bulbus origin and species identification. [Display omitted] • Geographic identification of lilii bulbus with stable isotopes and elements is feasible. • δ18O are useful factors for identifying lilii bulbus's geographical origin. • K, Ca, Na and Mg are important factors to distinguish different species of lilii bulbus samples. • The accuracy of OPLS-DA in discriminating lilii bulbus species is more than 89.61%. [ABSTRACT FROM AUTHOR]

[Display omitted] • Stable isotope and multi-element compositions of Huangjing was used for its origin traceability. • OPLS-DA significantly enhanced the traceability capability for Qingyang Huangjing products. • The discrimination accuracies of Qingyang (PGI products) were 100 % compared with other products. • Regional and farming patterns the correlations with eco-environment were discussed. • The strategy can be applied to combat mislabeling of PGI Huangjing products and ensure food safety. Huangjing (Polygonati Rhizoma) is a high-value homology of medicine and food in China, for which the geographical origins (i.e., geoherbalism) and farming patterns (natural or farmed) are decisive in its functions and selling price. In this study, five stable isotope ratios (δ 13C, δ 15N, δ 18O, δ 2H, and 87Sr/86Sr) and 48 multi-element signatures of 238 Huangjing samples collected from seven main production areas in China were analyzed together for origin traceability and farming authentication purposes. An analysis of variance (ANOVA) for each variable revealed that δ 18O, δ 2H, 87Sr/86Sr, Fe, and rare earth elements were significantly different among origins. The orthogonal partial least squares discriminant analysis (OPLS-DA) modeling of all variables indicated that K, Ca, Mg, Fe, and Mn were the most important variables (VIP > 1) for identifying Qingyang Huangjing protected geographical indication (PGI) from those of other origins, and discriminant accuracy reached 100 %. Moreover, ANOVA and OPLS-DA demonstrated that δ 15N, K, Ca, and Mg were the elements that effectively distinguished natural and farmed Huangjing. Therefore, the strategy may be an important alternative tool for authenticity management, which is significant for combating origin mislabeling and the fraudulent conducts of Huangjing products. [ABSTRACT FROM AUTHOR]