Present Address: Hochschule Geisenheim University, Geisenheim 65366, Germany.
Spectrochim Acta A Mol Biomol Spectrosc. 2021 Jan 5;244:118874. doi: 10.1016/j.saa.2020.118874. Epub 2020 Aug 23.
Excitation-emission matrix fluorescence (EEMF) spectroscopy is a simple and sensitive analytical technique. EEMF spectrum is essentially a collection of emission and excitation spectra acquired as increasing functions of excitation and emission wavelengths, respectively. EEMF spectral data sets produced per sample are highly correlated and larger in amount that need the assistance of chemometric techniques such partial least square (PLS) analysis if one desire to build robust calibration model. The objective of the PLS algorithm is to explain maximum variation of the spectral and concentration data matrices and to maximise the correlation between them. The application of a suitable variable selection technique can significantly improve the performance of PLS calibration model. Towards this, the present work proposes application of competitive adaptive reweighted sampling (CARS) as a variable selection approach prior to PLS analysis of EEMF spectral data sets. The utility of proposed approach was successfully demonstrated by analysing the significantly overlapped EEMF spectral data set of aqueous mixtures of Anthracene, Chrysene, Fluoranthene and Pyrene that are highly carcinogenic and mutagenic in nature. The developed procedure was also successfully used for the analysis of Chrysene and Pyrene mixtures in gasoline spiked ground water samples. The CARS assisted PLS model was also compared with full spectrum PLS, genetic algorithm assisted PLS, ant colony optimisation assisted PLS and N-way PLS models. The obtained results of the present work clearly indicated that application of PLS algorithm on CARS optimised EEMF spectral variables significantly improved the performance of the calibration models.
激发-发射矩阵荧光（EEMF）光谱学是一种简单而灵敏的分析技术。EEMF 光谱本质上是一组发射光谱和激发光谱的集合，分别作为激发和发射波长的递增函数获得。每个样品产生的 EEMF 光谱数据集高度相关且数量较大，如果希望构建稳健的校准模型，则需要借助化学计量学技术（如偏最小二乘法（PLS）分析）的帮助。PLS 算法的目的是解释光谱和浓度数据矩阵的最大变化，并使它们之间的相关性最大化。应用适当的变量选择技术可以显著提高 PLS 校准模型的性能。为此，本工作提出在 PLS 分析 EEMF 光谱数据集之前，应用竞争自适应重加权采样（CARS）作为变量选择方法。通过分析蒽、屈、荧蒽和芘的水溶液混合物的高度致癌和致突变性质的显著重叠的 EEMF 光谱数据集，成功证明了所提出方法的实用性。所开发的程序还成功用于分析加标地下水样品中的屈和芘混合物。还将 CARS 辅助的 PLS 模型与全光谱 PLS、遗传算法辅助的 PLS、蚁群优化辅助的 PLS 和 N 路 PLS 模型进行了比较。本工作的结果清楚地表明，在 CARS 优化的 EEMF 光谱变量上应用 PLS 算法显著提高了校准模型的性能。
