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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

机译:几种三阶校正算法应用于荧光激发 - 发射样本数据阵列的比较:水污染中多环芳烃的无干扰测定

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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. (C) 2018 Published by Elsevier B.V.
机译:基于三阶校正算法的三阶校正算法提出了一种具有高精度荧光激发 - 发射 - 样本数据阵列的三阶校正算法的水污染中多环芳烃(PAHS)的污染中的受干扰常规测定水污染中的三阶校正算法。具有更高的准确度和更好的预测能力而不是第二种-Order(三维)校正。交替加权残留量约束四射性分解(AWRCQLD),四肢线性并联分析(4-PARAFAC),交替罚款四肢性分解(APQLD)和交替的罚款三线性分解(APTLD)应用于亚苯甲酸根(ANA),萘(NAP)和芴(流感分别。 Fulvic酸在真实情况下严重影响PAHS测定,因此它被模拟为干扰剂,在不同体积的富葡萄酸模拟不同的干扰条件下测量PAHS的激发 - 发射荧光基质(EEM),以构建四通数据阵列。通过AWRCQLD,4-PARAFAC和APQLD分析四路谱数据,通过APTLD分析的三元EEM,结果表明,一方面,可以使用四向数据与第三个数据更准确地测量PAHS -Order校准而不是低阶。另一方面,AWRCQLD算法可以通过更高的恢复速率和较小的均方误差来更好地反映三阶优势的优势,而不是其他三阶或二阶校正算法。 (c)2018由elestvier b.v出版。

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