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几种三阶校正算法在荧光激发-发射-样品数据阵列中的应用比较:水污染中多环芳烃的无干扰测定。

Comparison of several third-order correction algorithms applied to fluorescence excitation-emission-sample data array: Interference-free determination of polycyclic aromatic hydrocarbons in water pollution.

机构信息

Measurement Technology and Instrument Key Lab of Hebei Province, Yanshan University, Qinhuangdao 066004, China.

Measurement Technology and Instrument Key Lab of Hebei Province, Yanshan University, Qinhuangdao 066004, China; Vocational and Technical College of Liuzhou, Liuzhou 545000, China.

出版信息

Spectrochim Acta A Mol Biomol Spectrosc. 2018 Dec 5;205:381-390. doi: 10.1016/j.saa.2018.07.045. Epub 2018 Jul 18.

Abstract

Interference-free determination of polycyclic aromatic hydrocarbons (PAHs) in water pollution is proposed based on third-order correction algorithms with quadrilinear component modeling applied to the constructed four way fluorescence excitation-emission-sample data array with higher accuracy and better predictive ability than second-order (three-dimension) correction. Alternating weighted residue constraint quadrilinear decomposition (AWRCQLD), quadrilinear parallel factor analysis (4-PARAFAC), alternate penalty quadrilinear decomposition (APQLD) and alternate penalty trilinear decomposition (APTLD) are applied to acenaphthene (ANA), naphthalene (NAP) and fluorene (FLU) respectively. Fulvic acid affects PAHs determination seriously in real-world situation, so it is simulated as an interfering agent. Excitation-emission fluorescence matrixes (EEMs) of PAHs are measured at different volumes of fulvic acid simulated different interference conditions, to construct a four-way data array. After the four-way spectra data is analyzed by AWRCQLD, 4-PARAFAC, and APQLD, three-way EEMs analyzed by APTLD, results show that, on the one hand, PAHs can be measured more accurately with four-way data combined with third-order calibration than lower-order. On the other hand, AWRCQLD algorithm can reflect the superiority of third-order advantage better with higher recovery rate and smaller root mean square error, than other third-order or second-order correction algorithms.

摘要

提出了一种基于四线性分量建模的三阶校正算法,用于构建四路荧光激发-发射-样品数据阵列,以实现对水污染中多环芳烃(PAHs)的无干扰测定,该方法比二阶(三维)校正具有更高的准确性和更好的预测能力。交替加权残差约束四线性分解(AWRCQLD)、四线性平行因子分析(4-PARAFAC)、交替惩罚四线性分解(APQLD)和交替惩罚三线性分解(APTLD)分别应用于苊(ANA)、萘(NAP)和芴(FLU)。腐殖酸在实际情况下严重影响 PAHs 的测定,因此将其模拟为干扰剂。在不同体积的腐殖酸模拟不同干扰条件下,测量 PAHs 的激发-发射荧光矩阵(EEMs),以构建四路数据阵列。用 AWRCQLD、4-PARAFAC 和 APQLD 对四路光谱数据进行分析后,用 APTLD 对三向 EEMs 进行分析,结果表明,一方面,与低阶方法相比,结合三阶校正的四路数据可以更准确地测量 PAHs。另一方面,AWRCQLD 算法可以更好地反映三阶优势的优越性,具有更高的回收率和更小的均方根误差,优于其他三阶或二阶校正算法。

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