College of Chemistry and Environmental Engineering, Yangtze University, Jingzhou 434023, China.
College of Life Sciences, Yangtze University, Jingzhou 434025, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2020 May 5;232:118173. doi: 10.1016/j.saa.2020.118173. Epub 2020 Feb 20.
Alternating trilinear decomposition (ATLD) method enables the qualitative and quantitative analysis of excitation-emission matrix fluorescence (EEMF) data acquired from complex samples. However, the impact of diverse background interferences from different sample sources on the performances of ATLD method has never been lucubrated. In this work, simulated and real EEMF data sets from different sample sources with diverse background interferences were collected and subjected to ATLD analysis. The performances of ATLD modeling individual and global EEMF data sets were comprehensively compared in terms of the resolved spectral profiles and quantitative results. It was found that ATLD method can use the same set of calibration samples to resolve and quantify multiple components of interest in multiple complex systems with diverse background interferences, regardless of individual or global modeling. The results revealed that the qualitative and quantitative results provided by ATLD method were affected neither by diversity of background interferences nor by data merging as long as the acquired EEMF data sets conform to the trilinear component model. This property of ATLD method can enrich the "second-order advantage", i.e. the term "unknown interferences" in the concept of "second-order advantage" refers to not only constant background interferences but also diverse background interferences, which will be certain to further expand the practicality of ATLD method in complex sample analysis, especially in the field of fluorescence spectroscopy.
交替三线性分解（ATLD）方法可用于对从复杂样品中获得的激发-发射矩阵荧光（EEMF）数据进行定性和定量分析。然而，来自不同样品源的不同背景干扰对 ATLD 方法性能的影响从未被深入研究过。在这项工作中，收集了来自不同样品源的模拟和真实 EEMF 数据集，并对其进行了 ATLD 分析。从多个具有不同背景干扰的复杂系统中，分别对 ATLD 建模个体和全局 EEMF 数据集的性能进行了综合比较，包括解析的光谱轮廓和定量结果。结果表明，ATLD 方法可以使用相同的校准样本集来解析和量化多个具有不同背景干扰的感兴趣的多个成分，无论是个体建模还是全局建模。结果表明，只要获得的 EEMF 数据集符合三线性成分模型，ATLD 方法提供的定性和定量结果就不会受到背景干扰多样性或数据合并的影响。ATLD 方法的这一特性可以丰富“二阶优势”，即“二阶优势”概念中的“未知干扰”不仅指恒定的背景干扰，还指多样的背景干扰，这必将进一步扩大 ATLD 方法在复杂样品分析中的实用性，特别是在荧光光谱学领域。
