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.

提出了基于三阶校正算法和四线性成分模型的无干扰测定水污染中多环芳烃的方法，该算法应用于构造的四向荧光激发-发射-样品数据阵列，具有比第二种方法更高的准确性和更好的预测能力阶（三维）校正。将交替加权残基约束四线性分解（AWRCQLD），四线性平行因子分析（4-PARAFAC），交替罚四线性分解（APQLD）和交替罚三线性分解（APTLD）应用于（ANA），萘（NAP）和芴（ FLU）。富勒酸在实际情况下会严重影响PAHs的测定，因此被模拟为干扰剂。在模拟干扰条件下，在不同体积的黄腐酸中测量PAHs的激发-发射荧光矩阵（EEM），以构建四向数据阵列。通过AWRCQLD，4-PARAFAC和APQLD对四向光谱数据进行分析后，通过APTLD对三向EEM进行分析，结果表明，一方面，通过四向数据与第三相结合，可以更准确地测量PAHs。阶校准比低阶校准。另一方面，与其他三阶或二阶校正算法相比，AWRCQLD算法可以更好地反映三阶优势的优越性，并具有更高的恢复率和更小的均方根误差。通过AWRCQLD，4-PARAFAC和APQLD对四向光谱数据进行分析后，通过APTLD对三向EEM进行分析，结果表明，一方面，通过四向数据与第三相结合，可以更准确地测量PAHs。阶校准比低阶校准。另一方面，与其他三阶或二阶校正算法相比，AWRCQLD算法可以更好地反映三阶优势的优越性，并具有更高的恢复率和更小的均方根误差。通过AWRCQLD，4-PARAFAC和APQLD对四向光谱数据进行分析后，通过APTLD对三向EEM进行分析，结果表明，一方面，通过四向数据与第三相结合，可以更准确地测量PAHs。阶校准比低阶校准。另一方面，与其他三阶或二阶校正算法相比，AWRCQLD算法可以更好地反映三阶优势的优越性，并具有更高的恢复率和更小的均方根误差。