Zheng Guang-Hui, Zhou Sheng-Lu, Wu Shao-Hua
School of Geographic and Oceanic Sciences, Nanjing University, Nanjing 210093, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2011 Jan;31(1):173-6.
In the present study, visible-near infrared reflectance spectroscopy (VNIR) measured in laboratory was evaluated for prediction of the content of As in soils. Calibrations between As and reflectance were developed using cross-validation under partial least squares regression (PLSR). Prediction accuracy was tested via separate validation samples. The reflectance was pre-processed by several techniques like smoothing, multiplicative scatter correction (MSC), Log(1/R), first/second derivative (F/ SD) and continuum removal (CR). The accuracy of prediction was evaluated with three statistics: coefficients of determination (R2), ratio of performance to deviation (RPD), and root mean square error of prediction (RMSEP). The results of calibration, cross-validation and prediction of different pre-processing techniques, spectral resolution and OM content were compared. MSC provided better prediction (Prediction R2 = 0.711, RPD = 1.827, RMSEP = 1.613) than other methods because it removed the effects of light scattering and sample thickness. All the results of different resolution are acceptable (Prediction 0.678 < R2 < 0.711, 1.750 < RPD < 1.827, 1.613 < RMSEP < 1.685). The prediction accuracy of subsets with lower OM content(Prediction R2 = 0.694, RPD = 1.697, RMSEP = 1.644) was better than that with higher content. The study indicates that it is feasible to predict As element in soils using reflectance spectroscopy and the prediction accuracy can be improved by pre-processing. Thus this new rapid and cost-effective technique can be used in the monitoring of soil contamination.
在本研究中，对实验室测量的可见 - 近红外反射光谱（VNIR）用于预测土壤中砷含量进行了评估。在偏最小二乘回归（PLSR）下使用交叉验证建立了砷与反射率之间的校准模型。通过单独的验证样本测试预测准确性。反射率采用了多种技术进行预处理，如平滑处理、多元散射校正（MSC）、Log(1/R)、一阶/二阶导数（F/SD）和连续统去除（CR）。用三种统计量评估预测准确性：决定系数（R2）、性能与偏差比（RPD）和预测均方根误差（RMSEP）。比较了不同预处理技术、光谱分辨率和有机碳（OM）含量的校准、交叉验证和预测结果。与其他方法相比，MSC提供了更好的预测结果（预测R2 = 0.711，RPD = 1.827，RMSEP = 1.613），因为它消除了光散射和样品厚度的影响。不同分辨率的所有结果都是可接受的（预测0.678 < R2 < 0.711，1.750 < RPD < 1.827，1.613 < RMSEP < 1.685）。有机碳含量较低子集的预测准确性（预测R2 = 0.694，RPD = 1.697，RMSEP = 1.644）优于含量较高的子集。该研究表明，利用反射光谱预测土壤中的砷元素是可行的，并且通过预处理可以提高预测准确性。因此，这种新的快速且经济高效的技术可用于土壤污染监测。
