Université Claude Bernard Lyon1, CNRS, Institut des Sciences Analytiques, ISA UMR 5280, France.
Université Claude Bernard Lyon1, CNRS, Institut des Sciences Analytiques, ISA UMR 5280, France.
Talanta. 2020 May 1;211:120674. doi: 10.1016/j.talanta.2019.120674. Epub 2019 Dec 28.
The combined LIBS and ICP HRMS analysis of 13 tea samples are studied in view of identification of tea geographical origin. The elemental signature provided by LIBS spectra is treated by principal component analysis followed by partial least square discriminant analysis and factorial discriminant analysis. Selected element lines are found efficient to discriminate most sample groups. Data analysis model is improved by variable selection and the isotopic ratio B/B was employed to improve the prediction capacity of the model. The alkaline earth: Ba, Ca, Mg, Sr and alkaline Rb, Na are easily detected by the LIBS system and these elements are important to classify sample according to their geographical origin. Minor elements like P, S, Fe, B … also bring discriminant information. A five clusters model gave best correct identification in a cross validation test (94.2%). This method also allowed to identify the origin of four unknown teas. In this study the use of FDA or PLS DA after the PCA examination of the LIBS/ICP MS data led to similar conclusions for fast classification of the tea samples and identification of the geographical origin of the four unknown teas.
本研究采用 LIBS 和 ICP-HRMS 联合分析了 13 个茶叶样品,旨在鉴定茶叶的地理来源。通过主成分分析(PCA)、偏最小二乘判别分析(PLS-DA)和因子判别分析(FDA)对 LIBS 光谱提供的元素特征进行处理。选择的元素谱线可有效区分大多数样品组。通过变量选择改进数据分析模型,并采用同位素比 B/B 提高模型的预测能力。LIBS 系统可轻松检测到碱土金属:Ba、Ca、Mg、Sr 和碱金属 Rb、Na,这些元素对于根据地理来源对样品进行分类非常重要。磷、硫、铁、硼等微量元素也提供了鉴别信息。在交叉验证测试中,五聚类模型给出了最佳的正确识别率(94.2%)。该方法还可用于识别四种未知茶叶的产地。在本研究中,在对 LIBS/ICP-MS 数据进行 PCA 分析之后,使用 FDA 或 PLS-DA 可快速对茶叶样品进行分类,并鉴定出四种未知茶叶的地理来源,得到了相似的结论。