BACKGROUND There is an urgent need to strengthen testing and certification of geographically iconic foods, and to use discriminatory science and technology to regulate and verify.Multielement and stable isotope analyses were combined to provide a new chemometric approach to improve the discrimination tea samples from different geographical origins.Different stoichiometric methods (PCA,HCA,PLS-DA,BP-ANN and LDA) were used to demonstrate this discrimination approach using Yongchuanxiuya tea samples in experimental test. RESULTS Multielement and stable isotope analyses of tea samples using inductively coupled plasma mass spectrometry and inductively coupled plasma optical emission spectrometer easily distinguished geographical origins.However, the clustering ability of the two unsupervised learning methods (PCA and HCA) were worse than that of the three supervised learning methods(PLS-DA,BP-ANN and LDA). BP-ANN and LDA with 100% recognition and prediction abilities,found to be better than PLS-DA.86 Sr and 112 Cd were the markers for enabling the successful classification of tea samples according to their geographical origins.Under the validation by 'blind'dataset, the prediction accuracies of the BP-ANN and LDA methods were all greater than 90%. The LDA method showed the best performance, with an accuracy of 100%. CONCLUSION In summary, determination of mineral elements and stable isotopes by using ICP-MS and ICP-OES techniques coupled with chemometrics methods especially LDA method is a good approach for improving the authenticating a diverse range of tea. This study contributes toward generalizing the use of fingerprinting mineral elements and stable isotopes as a promising tool for testing the geographic roots of tea and food worldwide. This article is protected by copyright. All rights reserved.

中文翻译：

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使用 ICP-MS 和 ICP-OES 技术结合化学计量学方法改进茶叶的地理原产地鉴别

背景技术迫切需要加强对地理标志性食品的检测和认证，并利用判别性科学技术进行规范和验证。多元素分析和稳定同位素分析相结合，为提高不同地域茶叶样品的判别能力提供了一种新的化学计量学方法。来源。不同的化学计量方法（PCA、HCA、PLS-DA、BP-ANN 和 LDA）在实验测试中使用永川秀雅茶样品证明了这种区分方法。结果 使用电感耦合等离子体质谱仪和电感耦合等离子体发射光谱仪对茶叶样品进行多元素和稳定同位素分析很容易区分地理来源。然而，两种无监督学习方法（PCA和HCA）的聚类能力比三种监督学习方法（PLS-DA、BP-ANN和LDA）差。BP-ANN 和 LDA 具有 100% 的识别和预测能力，发现优于 PLS-DA。86 Sr 和 112 Cd 是能够根据茶样品的地理来源成功分类的标志。在“盲法”验证下'数据集，BP-ANN和LDA方法的预测准确率均大于90%。LDA 方法表现出最好的性能，准确率为 100%。结论 总之，使用 ICP-MS 和 ICP-OES 技术结合化学计量学方法，特别是 LDA 方法测定矿物元素和稳定同位素是一种很好的方法，可以提高对各种茶叶的鉴别能力。这项研究有助于推广使用指纹矿物元素和稳定同位素作为测试全球茶叶和食物地理根源的有前途的工具。本文受版权保护。版权所有。