三维荧光光谱结合EEMD与SWATLD测定水中多环芳烃
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摘要
多环芳烃(PAHs)类物质具有致畸、致癌、致突变的性质,严重污染生态环境,进而对人类的健康及动植物生长造成威胁。PAHs通过排污、大气沉降、地表径流等各种循环途径进入水环境中,由于种类众多且化学性质相似,常规的检测方法如化学滴定法、电化学法等很难实现快速准确的测定。为实现复杂体系中PAHs的定性与定量,工作中基于三维荧光光谱分析法,结合集合经验模态分解(EEMD)去噪与自加权交替三线性分解(SWATLD)二阶校正,对超纯水以及池塘水环境中的苊(ANA)和萘(NAP)进行分析测定。首先选择合理的浓度配制样本,用FS920荧光光谱仪测得样品的三维荧光光谱,利用空白扣除法将光谱数据中的散射消除,得到真实的光谱数据。然后对去除散射的数据进行EEMD降噪处理,该方法具有自适应性强、参数设置简便的优点,能够去除嘈杂信息,提高数据信噪比,并将去噪参数与快速傅里叶变换、小波滤波和经验模态分解进行比较。最后用SWATLD算法以"数学分离"代替"化学分离",对超纯水和池塘水环境中光谱重叠的ANA和NAP进行定性识别和定量预测,该算法对组分数的选择不敏感,能够在未知干扰物共存情况下实现多组分目标分析物的同时检测,即具有"二阶优势",并将预测结果与平行因子分析进行比较。结果表明空白扣除法能够成功将拉曼散射消除。EEMD降噪方法使ANA和NAP的光谱更加规整平滑,有效信息更加突出,该方法去噪后数据信噪比为16.845 2,均方根误差为11.136 6,波形相似系数为0.990 9,三项指标均优于快速傅里叶变换和经验模态分解等其他去噪方法,能达到小波滤波的去噪效果并且不用设置先验参数。利用SWATLD二阶校正方法得到验证样本中ANA与NAP的分解光谱与实际光谱基本吻合,平均预测回收率分别为96.4%和104.2%,预测均方根误差分别为0.105和0.092μg·L~(-1);在存在未知干扰物的池塘水样本中,分解出的光谱依然能与实际光谱吻合, ANA与NAP两者的平均预测回收率分别为94.8%和105.5%,预测均方根误差分别为0.067和0.169μg·L~(-1);与平行因子分析相比,两项指标均具有优势。
Polycyclic aromatic hydrocarbons(PAHs) have teratogenic, carcinogenic and mutagenic properties, which seriously pollute the ecological environment and threaten the health of human beings and the growth of animals and plants. PAHs enter into the water environment through various ways, such as sewage discharge, atmospheric settlement and surface runoff. Because of their large variety and similar chemical properties, it is difficult to achieve rapid and accurate determination of the conventional methods, such as chemical titration and electrochemical methods. In order to realize the qualitative and quantitative analysis of the PAHs in complex system, this article is based on the three-dimensional fluorescence spectrum analysis, and combined with the ensemble empirical mode decomposition(EEMD) which is used to de-noise, and the self-weighted alternating trilinear decomposition(SWATLD) which is used to do two order correction, the ANA and NAP in water and pond water environment were analyzed and measured. The sample is prepared by a reasonable concentration, and the three-dimensional fluorescence spectrum of the sample is measured by the FS920 fluorescence spectrometer. The real spectral data can be obtained by eliminating the scattering of the spectral data by the blank deduction method. Then the EEMD is carried out to remove the noisy information and improve the signal to noise ratio. This method has the advantages of strong self-adaptive and simple parameter setting. The denoising parameters were compared with fast Fourier transform, wavelet filtering and empirical mode decomposition. Finally, using "mathematical separation" instead of "chemical separation", the SWATLD algorithm is used to identify and predict the ANA and NAP in ultra pure water and pond water environment. The algorithm is not sensitive to the selection of the group fraction, and can be used to detect the multi component object simultaneously under the coexistence of unknown interferon. It has the "two-order advantage", and the prediction results are compared with parallel factor analysis. The results show that the EEMD method makes the spectrum of ANA and NAP more regular and smooth, and the effective information is more prominent. The signal to noise ratio of the de-noised data is 16.845 2, the root mean square error is 11.136 6, the waveform similarity coefficient is 0.990 9, and this three indexes are better than the other de-noising methods, such as fast Fourier transform and empirical mode decomposition. It can achieve the denoising effect of wavelet filtering without setting a priori parameter. Using the SWATLD two order correction method, the decomposition spectra of ANA and NAP in the verified samples are basically consistent with the actual spectra, and the average predicted recovery rates are 96.4% and 104.2% respectively. The predicted mean square root errors are 0.105 and 0.092 μg·L~(-1) respectively. In the pool water samples with unknown interferon, the decomposition spectrum is still consistent with the actual spectrum. The average prediction recovery of ANA and NAP is 94.8% and 105.5%, respectively, and the root mean square root error is 0.067 and 0.169 μg·L~(-1) respectively. Compared with the parallel factor analysis, this two indexes have the advantages.
Polycyclic aromatic hydrocarbons(PAHs) have teratogenic, carcinogenic and mutagenic properties, which seriously pollute the ecological environment and threaten the health of human beings and the growth of animals and plants. PAHs enter into the water environment through various ways, such as sewage discharge, atmospheric settlement and surface runoff. Because of their large variety and similar chemical properties, it is difficult to achieve rapid and accurate determination of the conventional methods, such as chemical titration and electrochemical methods. In order to realize the qualitative and quantitative analysis of the PAHs in complex system, this article is based on the three-dimensional fluorescence spectrum analysis, and combined with the ensemble empirical mode decomposition(EEMD) which is used to de-noise, and the self-weighted alternating trilinear decomposition(SWATLD) which is used to do two order correction, the ANA and NAP in water and pond water environment were analyzed and measured. The sample is prepared by a reasonable concentration, and the three-dimensional fluorescence spectrum of the sample is measured by the FS920 fluorescence spectrometer. The real spectral data can be obtained by eliminating the scattering of the spectral data by the blank deduction method. Then the EEMD is carried out to remove the noisy information and improve the signal to noise ratio. This method has the advantages of strong self-adaptive and simple parameter setting. The denoising parameters were compared with fast Fourier transform, wavelet filtering and empirical mode decomposition. Finally, using "mathematical separation" instead of "chemical separation", the SWATLD algorithm is used to identify and predict the ANA and NAP in ultra pure water and pond water environment. The algorithm is not sensitive to the selection of the group fraction, and can be used to detect the multi component object simultaneously under the coexistence of unknown interferon. It has the "two-order advantage", and the prediction results are compared with parallel factor analysis. The results show that the EEMD method makes the spectrum of ANA and NAP more regular and smooth, and the effective information is more prominent. The signal to noise ratio of the de-noised data is 16.845 2, the root mean square error is 11.136 6, the waveform similarity coefficient is 0.990 9, and this three indexes are better than the other de-noising methods, such as fast Fourier transform and empirical mode decomposition. It can achieve the denoising effect of wavelet filtering without setting a priori parameter. Using the SWATLD two order correction method, the decomposition spectra of ANA and NAP in the verified samples are basically consistent with the actual spectra, and the average predicted recovery rates are 96.4% and 104.2% respectively. The predicted mean square root errors are 0.105 and 0.092 μg·L~(-1) respectively. In the pool water samples with unknown interferon, the decomposition spectrum is still consistent with the actual spectrum. The average prediction recovery of ANA and NAP is 94.8% and 105.5%, respectively, and the root mean square root error is 0.067 and 0.169 μg·L~(-1) respectively. Compared with the parallel factor analysis, this two indexes have the advantages.
引文
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