Ma Changfei, Zhai Lulu, Ding Jianming, Liu Yanli, Hu Shunfan, Zhang Tianlong, Tang Hongsheng, Li Hua
Key Laboratory of Synthetic and Natural Functional Molecular of the Ministry of Education, College of Chemistry & Material Science, Northwest University, Xi'an 710127, China.
HBIS Materials Technology Research Institute, Shijiazhuang, Hebei 050000, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2024 Apr 5;310:123953. doi: 10.1016/j.saa.2024.123953. Epub 2024 Jan 24.
Polycyclic aromatic hydrocarbons (PAHs) contained in a large amount of oily sludge produced in petroleum and petrochemical production has become one of the main environmental protection concerns in the industry. The accurate determination of PAHs is of great significance in the field of petroleum geochemistry and environmental protection. In this study, Raman spectroscopy combined with partial least squares (PLS) based on different hybrid spectral preprocessing methods and variable selection strategies was proposed for quantitative analysis of phenanthrene, fluoranthrene, fluorene and naphthalene (Phe, Flt, Flu and Nap) in oil sludge. At first, PAHs in oily sludge was extracted by solid-liquid extraction with methanol as extractant, and Raman spectra of 21 oily sludge samples were collected by portable Raman spectrometer. And then, the influence of first derivative (D1st), wavelet transform (WT) and their hybrid spectral preprocessing on the predictive performance of the PLS calibration model was discussed. Thirdly, biPLS (backward interval partial least squares) was used to optimize the input variables before and after the hybrid spectral preprocessing methods, and the influence of biPLS and the hybrid spectral preprocessing sequence on the predictive performance of the PLS calibration model was discussed. Finally, the predictive performance of the PLS calibration model was optimized according to the results of leave-one-out cross-validation (LOOCV) method. The results show that the biPLS-D1st-WT-PLS calibration model established by using biPLS first to select the characteristic variables, followed by hybrid spectral preprocessing of the characteristic variables, has better prediction performance for Flt (determination coefficient of prediction (R) = 0.9987, and the mean relative error of prediction (MREP) = 0.0606). For Phe, Flu and Nap, the WT-biPLS-PLS calibration model has a better predictive effect (R are 0.9995, 0.9996 and 0.9983, and MREP are 0.0426, 0.0719 and 0.0497, respectively). In general, portable Raman spectroscopy combined with PLS calibration model based on different hybrid spectral preprocessing and variable selection strategies has achieved good prediction results for quantitative analysis of four PAHs in oily sludge. It is a new strategy to firstly select the characteristic variables of the original spectra, and secondly to preprocess the characteristic variables by the hybrid spectral preprocessing, which will provide a new idea for the establishment of quantitative analysis methods for PAHs in oily sludge.
石油和石化生产过程中产生的大量含油污泥中所含的多环芳烃（PAHs）已成为该行业主要的环境保护问题之一。PAHs的准确测定在石油地球化学和环境保护领域具有重要意义。本研究提出基于不同混合光谱预处理方法和变量选择策略的拉曼光谱结合偏最小二乘法（PLS），用于定量分析油泥中的菲、荧蒽、芴和萘（Phe、Flt、Flu和Nap）。首先，以甲醇为萃取剂通过固液萃取法提取油泥中的PAHs，并用便携式拉曼光谱仪采集21个油泥样品的拉曼光谱。然后，讨论了一阶导数（D1st）、小波变换（WT）及其混合光谱预处理对PLS校正模型预测性能的影响。第三，使用反向间隔偏最小二乘法（biPLS）在混合光谱预处理方法前后优化输入变量，并讨论biPLS和混合光谱预处理顺序对PLS校正模型预测性能的影响。最后，根据留一法交叉验证（LOOCV）方法的结果优化PLS校正模型的预测性能。结果表明，先采用biPLS选择特征变量，再对特征变量进行混合光谱预处理所建立的biPLS-D1st-WT-PLS校正模型对Flt具有较好的预测性能（预测决定系数（R）=0.9987，预测平均相对误差（MREP）=0.0606）。对于Phe、Flu和Nap，WT-biPLS-PLS校正模型具有较好的预测效果（R分别为0.9995、0.9996和0.9983，MREP分别为0.0426、0.0719和0.0497）。总体而言，基于不同混合光谱预处理和变量选择策略的便携式拉曼光谱结合PLS校正模型对油泥中4种PAHs的定量分析取得了良好的预测结果。先选择原始光谱的特征变量，再通过混合光谱预处理对特征变量进行处理，这是一种新策略，将为建立油泥中PAHs定量分析方法提供新思路。
