多环芳烃(PAHs)是一类在自然环境中常见且广泛存在的有毒有害有机物。 其主要来源有自然界的各种微生物以及植物的生物合成, 富含植被区域的天然火灾, 火山的喷发物, 化石燃料以及人为工业碳氢化合物的不完全燃烧和运输过程中的石油泄漏等。 多环芳烃的毒性较为强烈, 具有生物致癌性, 遗产毒性和致突变性。 它对于人体呼吸系统, 循环系统, 神经系统有着多方面的危害, 是一种重要的有机污染物, 因此有必要对多环芳烃的现场监测和分析方法进行研究。 目前对于多环芳烃的分析方法主要有化学分析法和光谱分析法。 化学分析法包含有前处理的化学滴定法, 液相色谱法(LC), 高效液相色谱法(HPLC), 气相色谱质谱法(GC-MS); 光谱学分析法涉及紫外吸收光谱, 荧光光谱和三维荧光光谱等。 三维荧光光谱同时获得激发波长和发射波长的信息, 因而包含的光学信息十分丰富, 灵敏度高, 光谱特征显著, 在实际水体的现场检测和水体样本混合组分的快速研究有明显的优势。 常见的三维荧光光谱解析方法有平行因子分析法(PARAFAC), 多维偏最小二乘法(N-PLS)等。 平行因子分析是分析多环芳烃重叠三维荧光光谱的一种有效方法。 但有时由于多种组分的荧光较弱, 它对三维荧光光谱的欠定分析并不能得到令人满意的结果。 为了从两个样品中提取更多的成分, 提出一种基于奇异值分解(SVD)和PARAFAC的方法。 首先对每个观测样本进行奇异值分解, 根据累积贡献率选取合适的奇异值, 构造新的伪样本来突出微弱的荧光信号。 然后, 将两个观测样品及其对应的伪样品输入PARAFAC, 恢复组分光谱。 为验证所提方法的有效性, 对三组不同荧光强度的多环芳烃重叠三维荧光光谱进行了分析。 结果表明, 从两个混合样品中提取并识别出6个多环芳烃的纯组分光谱, 其分辨发射和激发光谱与标准光谱的相似性均在0.80以上。

Polycyclic aromatic hydrocarbons (PAHs) are a toxic and harmful organic compound that widely exists in the natural environment. Its main sources are various microorganisms in nature and plant biosynthesis, natural fires in vegetation-rich areas, volcanic eruptions, fossil fuels, incomplete combustion of artificial industrial hydrocarbons and oil leakage during transportation. Polycyclic aromatic hydrocarbons are toxic, with biological carcinogenicity, heritage toxicity and mutagenicity. It harms human respiratory, circulatory and nervous systems in many aspects. Therefore, it is necessary to study the on-site monitoring and analysis methods of polycyclic aromatic hydrocarbons. Chemical analysis methods include chemical titration with pretreatment, liquid chromatography (LC), high-performance liquid chromatography (HPLC), and gas chromatography-mass spectrometry (GC-MS);Spectroscopic analysis studies UV absorption spectra, fluorescence spectra and three-dimensional fluorescence spectra. The three-dimensional fluorescence spectrum obtains the information of excitation and emission wavelength simultaneously, so it contains more optical information, high sensitivity and remarkable spectral characteristics. Therefore, it gains obvious advantages in the field detection of actual water bodies and the rapid study of mixed components of water samples. Common three-dimensional fluorescence spectrum analysis methods include parallel factor analysis (PARAFAC), multidimensional partial least squares (N-PLS), etc. Parallel factor analysis is an effective method to analyze polycyclic aromatic hydrocarbons' overlapping three-dimensional fluorescence spectra. However, sometimes, due to the weak fluorescence signal, the underdetermined three-dimensional fluorescence spectrum analysis cannot get satisfactory results. In order to extract more components from two sample sets, a method based on singular value decomposition(SVD)and PARAFAC is proposed. First, singular value decomposition is used on each observed sample, the appropriate singular value is selected according to the cumulative contribution rate, and a new pseudo sample is constructed to highlight the weak fluorescence signal. They were then inputting two observed samples and their corresponding pseudo samples into PARAFAC to recover the component spectrum. Three groups' three-dimensional fluorescence spectra of polycyclic aromatic hydrocarbons with different fluorescence intensities are analyzed. The pure component spectra of six polycyclic aromatic hydrocarbons were extracted and identified from two mixed samples and the result shows that the similarity between the emission/excitation spectra, and the standard spectra is more than 0.80.