Ramakrishna Mission Vivekananda University, Kolkata 700103, India.
Department of Plant and Soil Science, Texas Tech University, Box 42122, Lubbock, TX 79409, USA.
Environ Pollut. 2014 Jul;190:10-8. doi: 10.1016/j.envpol.2014.03.005. Epub 2014 Mar 29.
This pilot study compared penalized spline regression (PSR) and random forest (RF) regression using visible and near-infrared diffuse reflectance spectroscopy (VisNIR DRS) derived spectra of 164 petroleum contaminated soils after two different spectral pretreatments [first derivative (FD) and standard normal variate (SNV) followed by detrending] for rapid quantification of soil petroleum contamination. Additionally, a new analytical approach was proposed for the recovery of the pure spectral and concentration profiles of n-hexane present in the unresolved mixture of petroleum contaminated soils using multivariate curve resolution alternating least squares (MCR-ALS). The PSR model using FD spectra (r(2) = 0.87, RMSE = 0.580 log10 mg kg(-1), and residual prediction deviation = 2.78) outperformed all other models tested. Quantitative results obtained by MCR-ALS for n-hexane in presence of interferences (r(2) = 0.65 and RMSE 0.261 log10 mg kg(-1)) were comparable to those obtained using FD (PSR) model. Furthermore, MCR ALS was able to recover pure spectra of n-hexane.
本初步研究对比了使用经两种不同光谱预处理（一阶导数（FD）和标准正态变量（SNV），随后进行去趋势处理）后的 164 个石油污染土壤的可见-近红外漫反射光谱（VisNIR DRS），通过 Penalized Spline 回归（PSR）和随机森林（RF）回归两种方法，对土壤中石油污染进行快速定量。此外，提出了一种新的分析方法，即通过多变量曲线分辨交替最小二乘法（MCR-ALS），从石油污染土壤未解析混合物中回收纯的正己烷光谱和浓度分布。使用 FD 光谱的 PSR 模型（r(2) = 0.87，RMSE = 0.580 log10 mg kg(-1)，残差预测偏差 = 2.78）表现优于所有其他测试模型。在存在干扰的情况下，MCR-ALS 对正己烷的定量结果（r(2) = 0.65 和 RMSE 0.261 log10 mg kg(-1)）与使用 FD（PSR）模型相当。此外，MCR-ALS 能够恢复正己烷的纯光谱。
