三维荧光光谱结合多元校正分析对石油类污染物复杂多组分体系测定方法多谱图混叠, 且易受到空白荧光和干扰物荧光影响降低了测定准确性。 提出在三维荧光光谱中增加一维温度信息构造激发波长-发射波长-温度-样品(EEM-temperature data array)的四维荧光光谱数据阵列, 应用四线性成分模型建立高维荧光光谱定性定量分析的方法。 实验证明在15～25 ℃温度范围内, 矿物油荧光光谱轮廓形状不随温度变化, 而其强度随温度线性变化, 满足四线性要求, 这为构建四维荧光光谱发展高维数据的三阶校正提取更丰富的有效信息提供了可能。 三阶校正不仅可以在干扰物共存的情况下对感兴趣组份进行定量测定, 即具有“二阶优势”, 还具有更高的选择性和灵敏性, 可以对高共线性和背景干扰的重叠光谱表现更好的解析能力, 即“三阶优势”。 对0#柴油、 97#汽油和机油为混合油待测组分, 腐殖酸为水体干扰组分组成的复杂体系污染油样品为进行实验, 得到的三维荧光光谱利用平行因子(PARAFAC)算法和交替惩罚三线性分解(APTLD)算法进行二阶校正分析, 将三维荧光光谱在温度方向上堆叠构成增加温度维度的四维荧光光谱数阵, 并将其利用四维平行因子算法(4-PARAFAC)和交替惩罚四线性分解(APQLD)算法进行三阶校正分析, 比较, 0#柴油、 97#汽油和机油的预测结果表明增加了影响荧光光谱的温度因素构造的四维荧光光谱提高了有效信息提取能力, 四维荧光光谱结合高阶校正算法能提高油种光谱识别和浓度精确检测, 较传统的三维荧光光谱分析提高了回收率(recovery rate)和预测均方根误差(root mean square error of prediction, RMSEP), 有利于石油类污染物的有效, 准确, 实时, 绿色环保检测。 同时指出了4-PARAFAC和APQLD算法各自的特点及其不同适用环境, 为油类污染物检测具体情况提供算法选择依据。 引入温度参量的四维荧光光谱结合三阶校正算法的检测技术较三维荧光光谱技术, 在组分光谱定性分辨和浓度定量检测方面能对复杂体系油类污染物实现快速有效, 绿色无污染地检测, 实现“数学分离”更有效代替“化学分离”。

Three dimensional fluorescence spectroscopy combined with multivariate calibration analysis for petroleum pollutants multicomponent determination method has problems of complex spectra aliasing, and is susceptible to blank fluorescence and interference fluorescence reducing the accuracy of result. Temperature information as one dimension added to the traditional three dimensional fluorescence spectrum to construct excitation wavelength-emission wavelength-temperature-samples four dimensional fluorescence spectrum data array (excitation-emission-temperature-sample data array, EEM-temperature data array) was proposed, and four linearity component model combined with high dimensional fluorescence spectrum qualitative and quantitative analysis was applied. Experiments demonstrate that fluorescence spectral shape of mineral oil does not change with the change of temperature in 15 to 25 ℃ range, but the intensity changes linearily, satisfying the requirement of four linearity, providing possibility for developing four dimensional fluorescence spectra with third-order correction to extract more useful information from the high dimensional data. The third order correction not only has “second order advantage”, namely to quantitatively determine interesting constituents in the presence of interferences, but also has higher selectivity and sensitivity, higher resolution ability for colinearity and background interference of the overlapping spectra, namely, “third order advantage”. The complex petroleum pollution system composed of 0# diesel, 97# gasoline and engine oil as components to be determined and humic acid as water interference component are experimental samples. The parallel factor (PARAFAC) algorithm and the alternating penalty trilinear decomposition (the alternating penalty trilinear decomposition, APTLD) algorithm are applied to the three dimensional fluorescence spectra for the second-order calibration analysis; the four dimensional fluorescence spectra data array containing temperature information is constructed by stacking three dimensional fluorescence spectra along temperature direction dimension, and is analyzed by four dimensional parallel factor algorithm (4-PARAFAC) and alternating penalty quadrilinear decomposition (alternating penalty quadrilinear decomposition, APQLD) for third-order correction analysis. The prediction results of 0# diesel, 97# gasoline and engine oil are compared and show that the four way fluorescence spectrum with adding the affecting factor of temperature increases the extraction ability for effective information and the four dimensional fluorescence spectroscopy combined with high-order correction algorithm can improve the oil spectrum recognition and concentration detection precision and improve the recovery rate and the root mean square prediction error (root mean square error of prediction, RMSEP) compared with the traditional three dimensional fluorescence spectrum analysis, advantageous to the effective, accurate, real-time, green environmental detection for petroleum pollutants. At the same time, the characteristics of 4-parafac and APQLD algorithms and their different applicable environments are pointed out, which can provide a basis for the algorithm selection for the detection of oil pollutants. The four-dimensional fluorescence spectra with introduction of the temperature parameters combined with third-order correction algorithm detection technology, no matter in qualitative resolution of constituent spectra or quantitative concentration determination of the complex system of oil pollution compared with three-dimensional fluorescence spectrum technology, is capable of realizing fast, effective, green and pollution-free detection, thus “mathematical separation” can replace “chemical separation” more effectively.