三维荧光光谱法在研究多环芳烃(PAHs)类物质的荧光信息时起到了重要作用。 多环芳烃类物质具有致癌性, 难降解性, 多由尾气排放, 垃圾焚烧产生, 危害着人类健康及环境, 因此人们不断探索对多环芳烃检测的方法。 实验选取多环芳烃中的苊和萘作为检测物质, 利用FLS920荧光光谱仪, 为避免荧光光谱仪本身产生的瑞利散射影响, 设置起始的发射波长滞后激发波长40 nm, 设置扫描的激发波长( λ_ex)范围为: 200~370 nm, 发射波长( λ_em)范围为: 240~390 nm, 对多环芳烃进行荧光扫描获取荧光数据, 采用三维荧光光谱技术结合平行因子算法对混合溶液中的苊和萘进行定性定量分析。 实验选用的苊和萘均购于阿拉丁试剂官网, 配制浓度为10 mg·L^-1的一级储备液, 再将一级储备液稀释, 得到苊和萘浓度为0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4和4.5 mg·L^-1的二级储备液, 并将苊和萘进行混合。 在进行光谱分析前需要对苊和萘的光谱进行预处理, 采用空白扣除法扣除拉曼散射的影响, 并采用集合经验模态分解(EEMD)消除干扰噪声。 实验测得苊存在两个波峰, 位于 λ_ex=298 nm, λ_em=324/338 nm处, 萘存在一个波峰, 位于 λ_ex=280 nm, λ_em=322 nm处。 选用的PARAFAC算法对组分数的的选择很敏感, 因此采用核一致诊断法预估组分数, 估计值2和3的核一致值都在60%以上, 分别对混合样品进行了2因子和3因子的PARAFAC分解, 将分解后得到的激发发射光谱数据和各组分浓度数据进行归一化处理, 并绘制光谱图, 与归一化处理后的真实的激发发射光谱图和各组分浓度图进行对比。 同时将PARAFAC得到的混合样本的预测浓度, 通过计算回收率( R)和均方根误差(RMSEP)来判定定量分析的准确度。 选择2因子时, 各混合样品中苊和萘拟合度为95.7%和96.7%, 平均回收率分别为101.8%和98.9%, 均方根误差分别为0.018 7和0.031 6; 选择3因子时, 各混合样品中苊和萘拟合度为95.3%和95.8%, 平均回收率分别为97%和102.5%, 均方根误差分别为0.033和0.116, 由三项指标可得选用2因子进行定性定量分析的效果明显好于选用3因子。 分析实验结果表明, 基于三维荧光光谱法和PARAFAC算法对混合样品进行定性定量分析, 能够有效的判定混合样品的类别, 同时能够成功的预测出混合样品的浓度。

Three-dimensional fluorescence spectroscopy plays an important role in studying the fluorescence information of polycyclic aromatic hydrocarbons (PAHs). PAHs are carcinogenic and refractory. They are mostly produced by exhaust emissions and waste incineration, which endanger human health and the environment. Therefore, people are constantly exploring the detection methods of PAHs. ANA and NAP in PAHs were selected as detection substances and FLS920 fluorescence spectrometer was used in the experiment. In order to avoid the influence of Rayleigh scattering produced by the fluorescence spectrometer itself, the initial emission wavelength was set at 40 nm, and the excitation wavelength was lagged behind, and the scanning excitation wavelength (lambda ex) was set at 200~370 nm, and the emission wavelength (lambda em) was set at 240~390 nm. Then we could gain the fluorescence data of PAHs obtained by fluorescence scanning, and we could analyze ANA and NAP qualitatively and quantitatively in mixed solution by the three-dimensional fluorescence spectroscopy and PARAFAC. The ANA and NAP used in the experiment were purchased from the Aladdin reagent official website, and we prepared a stock solution with a concentration of 10 mg·L^-1, and we should dilute the stock solution, and we canget 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5 mg·L^-1 of secondary stock solution, which obtain a concentration of ANA and NAP, Then we maxed the solution of ANA and NAP. Before spectral analysis, the spectra of ANA and NAP needed to be pretreated, and we should eliminate the effect of Raman scattering by blank subtraction method, and adopt the way of ensemble empirical mode decomposition (EEMD) to eliminate interference noise. In this experiment, there are two peaks in ANA, located at λ_ex=298 nm, λ_em=324/338 nm, and the peaks of NAP at λ_ex=280 nm and λ_em=322 nm. The PARAAFAC algorithm selected in this paper was very sensitive to the choice of component number, therefore, using the method of nuclear consistency diagnosis to estimate the number of components, and the nuclear consistency values of the estimated values 2 and 3 were all over 60%, then decomposed the mixed samples by PARAFAC of 2 and 3 factors respectively. After decomposition, the data of excitation emission spectra and concentration of each component were normalized, and we can draw the spectrogram, and compare with the real excitation emission spectrogram and concentration map of each component. At the same time, the predicted concentration of mixed samples obtained by PARAFAC was used to determine the accuracy of quantitative analysis by calculating the recovery rate ( R) and root mean square error (RMSEP). When choosing two factors, the fitness of ANA and NAP was 95.7% and 96.7%, the average recovery was 101.8% and 98.9%, the root mean square error was 0.018 7 and 0.031 6, and choosing three factors, the fitness of ANA and NAP was 95.3% and 95.8%, the average recovery was 97% and 102.5%, the root mean square error was 0.033 and 0.116. Because of the three indicators, the effect of qualitative and quantitative analysis with two factors was better than that with three factors. The experimental results showed that the qualitative and quantitative analysis of mixed samples based on three-dimensional fluorescence spectrometry and PARAFAC algorithm can effectively determine the type of mixed samples, and its can successfully predict the concentration of mixed samples.