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基于非负因子(NNF)辅助的激发-发射矩阵荧光光谱数据集的偏最小二乘法(PLS)分析:自动识别和量化多荧光体混合物。

Non-negative Factor (NNF) Assisted Partial Least Square (PLS) Analysis of Excitation-Emission Matrix Fluorescence Spectroscopic Data Sets: Automating the Identification and Quantification of Multifluorophoric Mixtures.

机构信息

Hochschule Geisenheim University, 65366, Geisenheim, Germany.

出版信息

J Fluoresc. 2019 Sep;29(5):1183-1190. doi: 10.1007/s10895-019-02435-8. Epub 2019 Sep 10.

Abstract

Excitation-emission matrix fluorescence spectroscopy is simple and sensitive techniques that generate the composite fluorescence fingerprints. EEMF can be used for the identification and quantification of the fluorophores without involving any pre-separation step provided a suitable data analysis approach is applied. In the present work, non-negative factor (NNF) assisted partial least square (PLS) analysis is used for the analysis of EEMF data sets acquired for the dilute aqueous mixtures of fluorophores. The proposed approach allows automatic selection of the optimum number of factors for NNF analysis by incorporating the Akaike information criterion. The proposed approach also incorporates the spectral correlation analysis for the automatic identification of the NNF retrieved EEMF spectral profiles. The NNF retrieved contribution values along with their real concentration values are subjected to PLS analysis to develop a calibration model. The proposed approach was successfully tested using EEMF data acquired for the dilute aqueous mixtures of Catechol, Hydroquinone, Indole, Tryptophan and Tyrosine. The results were evaluated using the various statistical parameters and each of them found to well within the expected limits. In summary, NNF assisted PLS analysis of EEMF technique allows automatized analysis of the multifluorophoric mixtures with minimum user inputs.

摘要

激发-发射矩阵荧光光谱学是一种简单而灵敏的技术,它生成复合荧光指纹。EEMF 可用于荧光团的识别和定量,而无需进行任何预先分离步骤,只要应用适当的数据分析方法即可。在本工作中,非负因子 (NNF) 辅助偏最小二乘 (PLS) 分析用于分析稀水溶液混合物中荧光团的 EEMF 数据集。所提出的方法通过结合赤池信息量准则,允许自动选择 NNF 分析的最佳因子数。所提出的方法还结合了光谱相关分析,用于自动识别 NNF 检索的 EEMF 光谱轮廓。NNF 检索的贡献值及其真实浓度值被提交给 PLS 分析,以建立校准模型。该方法使用 Catechol、Hydroquinone、Indole、Tryptophan 和 Tyrosine 的稀水溶液混合物获得的 EEMF 数据进行了成功测试。使用各种统计参数评估结果,发现它们都在预期范围内。总之,EEMF 技术的 NNF 辅助 PLS 分析允许在最小用户输入的情况下自动分析多荧光团混合物。

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