Li Haonan, Li Maogang, Tang Hongsheng, Li Hua, Zhang Tianlong, Yang Xiao-Feng
Key Laboratory of Synthetic and Natural Functional Molecule of the Ministry of Education, College of Chemistry & Materials Science, Northwest University Xi'an 710127 China
College of Chemistry and Chemical Engineering, Xi'an Shiyou University Xi'an 710065 China.
RSC Adv. 2023 Mar 22;13(14):9353-9360. doi: 10.1039/d2ra08279a. eCollection 2023 Mar 20.
Polycyclic aromatic hydrocarbons (PAHs) are typical organic pollutants in soil and are teratogenic and carcinogenic. Therefore, rapid and accurate analysis of PAHs in soil can provide a theoretical basis and data support for soil contamination risk assessment. In this work, a fluorescence spectroscopy technique combined with partial least squares (PLS) was proposed for rapid quantitative analysis of phenanthrene (PHE) in soil. At first, the fluorescence spectra of 29 soil samples with different concentrations (0.3-10 mg g) of PHE were collected by RF-5301 PC fluorescence spectrophotometer. Secondly, the effects of different spectral preprocessing methods were investigated on the prediction performance of the PLS calibration model. And then, the influence of competitive adaptive reweighted sampling (CARS) wavelength points on the prediction performance of PLS calibration model was discussed. Finally, according to the selected wavelength points, a quantitative analytical model for PHE content in soil was constructed using the PLS calibration method. To further explore the predictive performance of the CARS-PLS calibration model, the predictive results were compared with those of the RAW spectrum-partial least squares calibration model (RAW-PLS) and the wavelet transform-standard normal variation (WT-SNV) calibration model. The CARS-PLS calibration model showed the optimal predictive performance and its coefficient of determination of cross-validation ( ) and root mean square error of 10-fold cross-validation (RMSEcv) were 0.9957 and 18.98%, respectively. The coefficient of determination of prediction set ( ) and root mean square error of prediction set (RMSEp) were 0.9963 and 16.13%, respectively. Hence, the CARS algorithm based on fluorescence spectrum coupled with PLS can give a rapid and accurate quantitative analysis of the PHE content in soil.
多环芳烃（PAHs）是土壤中典型的有机污染物，具有致畸性和致癌性。因此，快速准确地分析土壤中的多环芳烃可为土壤污染风险评估提供理论依据和数据支持。在本研究中，提出了一种结合偏最小二乘法（PLS）的荧光光谱技术，用于土壤中菲（PHE）的快速定量分析。首先，使用RF - 5301 PC荧光分光光度计收集了29个不同浓度（0.3 - 10 mg/g）菲的土壤样品的荧光光谱。其次，研究了不同光谱预处理方法对PLS校准模型预测性能的影响。然后，讨论了竞争性自适应重加权采样（CARS）波长点对PLS校准模型预测性能的影响。最后，根据选定的波长点，采用PLS校准方法构建了土壤中菲含量的定量分析模型。为了进一步探究CARS - PLS校准模型的预测性能，将预测结果与原始光谱 - 偏最小二乘校准模型（RAW - PLS）和小波变换 - 标准正态变量（WT - SNV）校准模型的预测结果进行了比较。CARS - PLS校准模型表现出最佳的预测性能，其交叉验证决定系数（ ）和10倍交叉验证均方根误差（RMSEcv）分别为0.9957和18.98%。预测集决定系数（ ）和预测集均方根误差（RMSEp）分别为0.9963和16.13%。因此，基于荧光光谱结合PLS的CARS算法能够对土壤中菲的含量进行快速准确的定量分析。
