Ma Changfei, Zhang Qun, Liang Jing, Yang Shan, Zhang Tianlong, Ruan Fangqi, 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.
College of Chemistry and Materials, Weinan Normal University, Weinan 714099, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2024 Oct 5;318:124531. doi: 10.1016/j.saa.2024.124531. Epub 2024 May 25.
Polycyclic aromatic hydrocarbons (PAHs) present in oily sludge generated by the petroleum and petrochemical industries have emerged as a prominent concern within the realm of environmental conservation. The precise determination of PAHs holds immense significance in both petroleum geochemistry and environmental protection. In this study, a combination of surface-enhanced Raman spectroscopy (SERS) and solid-liquid extraction was employed for the screening of PAHs in oily sludge. Methanol was utilized as the extraction solvent for PAHs, while nanosilver-silicon coupling substrates were employed for their detection. The SERS spectrum was acquired using a portable Raman spectrometer. The nano silver-silicon coupling substrate exhibits excellent uniformity, with relative standard deviations (RSDs) of Phenanthrene, Fluoranthrene, Fluorene and Naphthalene (Phe, Flt, Flu and Nap) being 2.8%, 1.08%, 1.41%, and 5.44% respectively. Moreover, the limits of detection (LODs) achieved remarkable values of 0.542 μg/g, 0.342 μg/g, 0.541 μg/g, and 5.132 μg/g. The quantitative analysis of PAHs in oily sludge was investigated using SERS technology combined with partial least squares (PLS). The optimal PLS calibration model was optimized by combining spectral preprocessing methods and using the SiPLS (Synergy interval partial least squares)-VIP (Variable Importance in Projection) hybrid variable selection strategy. The prediction performance of the D1st (First derivative)-WT (Wavelet transform)-SiPLS-VIP-PLS model was deemed satisfactory, as evidenced by high R values of 0.9851, 0.9917, and 0.9925 for Phe, Flt, and Flu respectively; additionally, the corresponding MREP values were found to be 0.0580, 0.0668, and 0.0669 respectively. However, for Nap analysis, the D1st-WT-PLS model proved to be a better calibration model with an R value of 0.9864 and an MREP (Mean relative error of prediction) value of 0.0713. In summary, SERS technology combined with PLS based on different spectral pretreatment methods and mixed variable selection strategies is a promising method for quantitative analysis of PAHs in oily sludge, which will provide new ideas and methods for the quantitative analysis of PAHs in oily sludge.
石油和石化行业产生的含油污泥中存在的多环芳烃（PAHs）已成为环境保护领域的一个突出问题。PAHs的精确测定在石油地球化学和环境保护中都具有极其重要的意义。在本研究中，采用表面增强拉曼光谱（SERS）和固液萃取相结合的方法对含油污泥中的PAHs进行筛选。甲醇用作PAHs的萃取溶剂，而纳米银-硅耦合基底用于其检测。使用便携式拉曼光谱仪采集SERS光谱。纳米银-硅耦合基底表现出优异的均匀性，菲、荧蒽、芴和萘（Phe、Flt、Flu和Nap）的相对标准偏差（RSD）分别为2.8%、1.08%、1.41%和5.44%。此外，检测限（LOD）分别达到了显著的0.542μg/g、0.342μg/g、0.541μg/g和5.132μg/g。采用SERS技术结合偏最小二乘法（PLS）对含油污泥中的PAHs进行定量分析。通过结合光谱预处理方法并使用SiPLS（协同区间偏最小二乘法）-VIP（投影变量重要性）混合变量选择策略对最佳PLS校准模型进行了优化。D1st（一阶导数）-WT（小波变换）-SiPLS-VIP-PLS模型的预测性能令人满意，Phe、Flt和Flu的R值分别为0.9851、0.9917和0.9925；此外，相应的MREP值分别为0.0580、0.0668和0.0669。然而，对于Nap分析，D1st-WT-PLS模型被证明是一个更好的校准模型，R值为0.9864，MREP（预测平均相对误差）值为0.0713。总之，基于不同光谱预处理方法和混合变量选择策略的SERS技术与PLS相结合是一种用于含油污泥中PAHs定量分析的有前途的方法，这将为含油污泥中PAHs的定量分析提供新的思路和方法。
