Pan Zhao, Wang Yu-tian, Shao Xiao-qing, Wu Xi-jun, Yang Li-li
Measurement Technology and Instrumentation Key Lab of Hebei Province, Yanshan University, Qinhuangdao 066004, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2012 Mar;32(3):714-8.
A method for identification and concentration measurement of petroleum pollutant by combining three-dimensional (3-D) fluorescence spectra with parallel factor analysis (PARAFAC) was proposed. The main emphasis of research was the measurement of coexisting different kinds of petroleum. The CCl4 solutions of a 0# diesel sample, a 97# gasoline sample, and a kerosene sample were used as measurement objects. The condition of multiple petroleum coexistence was simulated by petroleum solutions with different mixed ratios. The character of PARAFAC in complex mixture coexisting system analysis was studied. The spectra of three kinds of solutions and the spectra of gasoline-diesel mixed samples, diesel-kerosene mixed samples, and gas oline-diesel mixed with small counts of kerosene interference samples were analyzed respectively. The core consistency diagnostic method and residual sum of squares method were applied to calculate the number of factors in PARAFAC. In gasoline-diesel experiment, gasoline or diesel can be identified and measured as a whole respectively by 2-factors parallel factors analysis. In diesel-kerosene experiment, 2-factors parallel factors analysis can only obtain the characters of diesel, and the 3rd factor is needed to separate the kerosene spectral character from the mixture spectrum. When small counts of kerosene exist in gasoline-diesel solution, gasoline and diesel still can be identified and measured as principal components by a 2-factors parallel factor analysis, and the effect of interference on qualitative analysis is not significant. The experiment verified that the PARAFAC method can obtain characteristic spectrum of each kind of petroleum, and the concentration of petroleum in solutions can be predicted simultaneously, with recoveries shown in the paper. The results showed the possibility of petroleum pollutant identification and concentration measurement based on the 3-D fluorescence spectra and PARAFAC.
提出了一种将三维(3-D)荧光光谱与平行因子分析(PARAFAC)相结合的石油污染物识别与浓度测量方法。研究的重点是共存的不同种类石油的测量。以0#柴油样品、97#汽油样品和煤油样品的四氯化碳溶液作为测量对象。通过不同混合比例的石油溶液模拟多种石油共存的情况。研究了PARAFAC在复杂混合物共存体系分析中的特性。分别分析了三种溶液的光谱以及汽油 - 柴油混合样品、柴油 - 煤油混合样品和含少量煤油干扰的汽油 - 柴油混合样品的光谱。应用核心一致性诊断方法和残差平方和方法计算PARAFAC中的因子数。在汽油 - 柴油实验中,通过二因子平行因子分析可分别整体识别和测量汽油或柴油。在柴油 - 煤油实验中,二因子平行因子分析只能得到柴油的特性,需要第三因子来从混合光谱中分离煤油光谱特性。当汽油 - 柴油溶液中存在少量煤油时,通过二因子平行因子分析仍可将汽油和柴油作为主要成分进行识别和测量,干扰对定性分析的影响不显著。实验验证了PARAFAC方法能够获得每种石油的特征光谱,并可同时预测溶液中石油的浓度,文中给出了回收率。结果表明了基于三维荧光光谱和PARAFAC进行石油污染物识别与浓度测量的可能性。