Department of Environment, Ghent University, Coupure Links 653, 9000 Gent, Belgium.
Department of Green Chemistry and Technology, Ghent University, Frieda Saeysstraat 1, 9052 Gent, Belgium.
Sci Total Environ. 2022 Oct 1;841:156582. doi: 10.1016/j.scitotenv.2022.156582. Epub 2022 Jun 14.
Chemical analytical methods for metal analysis in soils are laborious, time-consuming and costly. This paper aims to evaluate the potential of short-range (SR) and full-range (FR) visible and infrared spectroscopy (vis-NIR) combined with linear and nonlinear calibration methods to estimate concentrations of nickel (Ni), cobalt (Co), cadmium (Cd), lead (Pb) and copper (Cu) in soils. A total of 435 soil samples were collected over agricultural sites, forest (7 %), pasture (5 %) and fallow land across a region in the northern part of Belgium. Generally, better predictions were obtained when using partial least squares regression (PLSR) and nonlinear calibration method [i.e., random forest (RF)] for processing of the spectral data, than when using support vector machine (SVM). FR generally outperformed SR and provided the best prediction results for Ni (R = 0.76), Co (R = 0.77), Cd (R = 0.64) and Pb (R = 0.65), when using PLSR and RF. SVM produced the best prediction result only for Pb (R = 0.57) using the SR spectra. The metals Ni, Co, Cd and Pb can be predicted successfully (good accuracy) from the FR vis-NIR spectra using PLSR for Co, and RF for Ni, Cd, Pb and Cu. Compared to the FR spectrophotometer, improvement in accuracy was obtained for Cd and Co, using the SR spectra when combined with PLSR and RF, respectively. It is concluded that the SR spectrometer can be used successfully for the prediction of Co with RF (R = 0.70), while it best predicted Cd with PLSR with an R value of 0.67, which is of value for regional survey.
土壤中金属分析的化学分析方法繁琐、耗时且昂贵。本文旨在评估短程（SR）和全谱（FR）可见/近红外光谱（vis-NIR）结合线性和非线性校准方法在估算土壤中镍（Ni）、钴（Co）、镉（Cd）、铅（Pb）和铜（Cu）浓度方面的潜力。共采集了 435 个土壤样本，分布在比利时北部地区的农业区、森林（7%）、牧场（5%）和休耕地。一般来说，使用偏最小二乘回归（PLSR）和非线性校准方法（即随机森林（RF））处理光谱数据时，预测结果优于支持向量机（SVM）。FR 通常优于 SR，并且当使用 PLSR 和 RF 时，提供了 Ni（R = 0.76）、Co（R = 0.77）、Cd（R = 0.64）和 Pb（R = 0.65）的最佳预测结果。SVM 仅使用 SR 光谱，为 Pb（R = 0.57）生成了最佳预测结果。使用 PLSR 为 Co 和 RF 为 Ni、Cd、Pb 和 Cu 从 FR vis-NIR 光谱成功预测了 Ni、Co、Cd 和 Pb 等金属（具有良好的准确性）。与 FR 分光光度计相比，当分别与 PLSR 和 RF 结合使用时，SR 光谱可提高 Cd 和 Co 的准确性。结论是，SR 光谱仪可成功用于预测 Co 的 RF（R = 0.70），而 PLSR 预测 Cd 的 R 值为 0.67，这对于区域调查具有价值。
