The aim of this study was to compare the performance of FT-NIR spectroscopy and near-infrared hyperspectral imaging (NIR-HSI) in predicting the C_org and N_t contents in mine soils. The mine soil samples were measured for the C_org and N_t contents and their NIR spectra were recorded (1000–2500 nm). Predictive models were developed using 126 samples with partial least square regression (PLSR) or artificial neural networks (ANN) and validated with 58 independent samples. The NIR-HSI based models had distinctly higher accuracy of C_org content prediction than those based on FT-NIR data in both PLSR and ANN methods, as indicated by lower of standard errors of prediction. The prediction accuracy for the N_t content was similar for the two spectral methods and both chemometric approaches tested. The study showed that despite lower spectral resolution the NIR-HSI spectra retained all the information needed for accurate prediction of C_org and N_t contents.

这项研究的目的是比较FT-NIR光谱学和近红外高光谱成像（NIR-HSI）在预测矿井土壤中C _org和N _t含量方面的性能。测量矿山土壤样品中的C _org和N _t含量，并记录其近红外光谱（1000-2500 nm）。使用具有偏最小二乘回归（PLSR）或人工神经网络（ANN）的126个样本开发了预测模型，并使用58个独立样本进行了验证。在PLSR和ANN方法中，基于NIR-HSI的模型比基于FT-NIR数据的模型具有更高的C _org含量预测准确性，这由较低的预测标准误差表明。N的预测精度两种光谱方法和两种化学计量方法的_t含量均相似。研究表明，尽管光谱分辨率较低，但NIR-HSI光谱保留了准确预测_Corg和_Nt含量所需的所有信息。