LI Shanjia, CUI Lijuan, SU Peixi, et al. Geographical Origin Traceability and Origin Confirmation of Specialty Agricultural Product Lanzhou Lily (Lilium davidii var. unicolor)[J]. Science and Technology of Food Industry, 2022, 43(16): 329−337. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2021110287.
Citation: LI Shanjia, CUI Lijuan, SU Peixi, et al. Geographical Origin Traceability and Origin Confirmation of Specialty Agricultural Product Lanzhou Lily (Lilium davidii var. unicolor)[J]. Science and Technology of Food Industry, 2022, 43(16): 329−337. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2021110287.

Geographical Origin Traceability and Origin Confirmation of Specialty Agricultural Product Lanzhou Lily (Lilium davidii var. unicolor)

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  • Received Date: November 23, 2021
  • Available Online: June 14, 2022
  • The place of production is closely related to the quality, safety and nutritional quality of Lanzhou lily (Lilium davidii var. unicolor), and its geographical origin traceability and origin confirmation is beneficial to the implementation of the protection of the place of production, the fidelity of characteristic products and the sustainable development of the industry. In this study, isotope ratio mass spectrometry (IRMS) and inductively coupled plasma mass spectrometry (ICP-MS) were used to determine the ratios of 3 stable isotopes (δ13C, δ15N, δ18O) and the content of 16 mineral elements (K, Mg, Ca, Na, B, Fe, Zn, Al, Mn, Cu, Mo, Cr, Cd, Se, As, Pb) in samples from the four main producing areas of Lanzhou lily. Combining principal component analysis (PCA), orthogonal partial least squares discrimination analysis (OPLS-DA) and linear discriminant analysis (linear discriminant analysis, LDA), the classification model of Lanzhou lily from different producing areas was constructed to verify the geographical origin traceability and origin confirmation. The results of the study showed that: δ13C, δ15N, K, Mg, Na, B, Fe, Mn, Cu, Mo, Cr, Cd were significantly different among the production areas (P<0.05). 5 principal components were extracted by PCA analysis, and the cumulative variance contribution rate was 84.36%. LDA original discrimination accuracy rate was 100%, leave-one-out cross validation (LOO-CV) discrimination accuracy rate was 88.89%. OPLS-DA model correct discrimination rate was 100%, of which the classification effect was optimal. This study would show that a multivariate statistical classification model based on the stable isotope and mineral element contents of Lanzhou lily could effectively distinguish lily from different production areas, and promote the establishment and improvement of its traceability system, and would be of great significance to the protection and quality control of Lanzhou lily's production area.
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