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Archive > Volume 15 Issue 1 > 2023,15(1):63-68. DOI:https://doi.org/10.1016/j.chmed.2022.05.005 Prev Next

基于ICP-OES多元素分析結(jié)合化學(xué)計(jì)量學(xué)多變量分析的青錢柳茶葉產(chǎn)地溯源

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Geographical discrimination of Cyclocarya paliurus tea for origin traceability based on multielement analysis by ICP-OES and chemometrics multivariate

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    摘要:
    目的：基于 ICP-OES 多元素分析和化學(xué)計(jì)量學(xué)多變量分析，本文重點(diǎn)研究了青錢柳茶葉的產(chǎn)地溯源。 方法：本研究采用 ICP-OES 法測定了11種微量元素的濃度，并通過多元統(tǒng)計(jì)分析處理。 結(jié)果：經(jīng)方差分析，6個(gè)產(chǎn)地中除Co外，其他10種元素的濃度差異顯著。Pearson 相關(guān)性分析顯示，11對元素呈正顯著相關(guān)，12對元素呈負(fù)顯著相關(guān)。結(jié)合 PCA?分析，利用這11個(gè)元素可以有效的區(qū)分其地理來源。S-LDA 模型的識別率為100%。 結(jié)論：ICP-OES多元素分析結(jié)合化學(xué)計(jì)量學(xué)多變量分析可以對茶葉的地理來源進(jìn)行追溯，為今后青錢柳的質(zhì)量控制和質(zhì)量評價(jià)提供參考。

    Abstract:
    Objective: This paper focused on the geographical discrimination of Cyclocarya paliurus tea for origin traceability based on multielement analysis by ICP-OES and chemometrics multivariate. Methods: In this study, eleven trace element concentrations were determined by ICP-OES and processed by multivariate statistical analysis. Results: Based on ANOVA, the mean concentrations of 10 elements except Co differed significantly among six origins. Pearson’s correlation analysis showed that 11 pairs of elements have a positive significant correlation and 12 pairs have a negative significant correlation. The geographical origins were effectively differentiated using the eleven elements combined with PCA. And the S-LDA model offered a 100% differentiation rate. Conclusion: The overall results suggested that the combination of multielement analysis by ICP-OES and chemometrics multivariate could trace the geographical origins of tea. And the paper can provide reference for quality control and quality evaluation of C. paliurus in the future.

    關(guān)鍵詞:
    化學(xué)計(jì)量學(xué)多變量分析;青錢柳;地理來源;多元素分析;ICP-OES

    Keywords:


    Project Supported:
    The project was supported by the National Natural Science Foundation of China (No. 81360631) and Project of the Key Research and Development Program for Jiangxi Province (No. 20203BBGL73218).

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