首页> 外文期刊>Geoderma: An International Journal of Soil Science >Estimation of total nitrogen and organic carbon contents in mine soils with NIR reflectance spectroscopy and various chemometric methods
【24h】

Estimation of total nitrogen and organic carbon contents in mine soils with NIR reflectance spectroscopy and various chemometric methods

机译:NIR反射光谱和各种化学测量方法估算矿井土壤中总氮和有机碳含量的估计

获取原文
获取原文并翻译 | 示例
           

摘要

Rapid analytical methods are needed to measure organic carbon (OC) and total nitrogen (N-t) contents in reclaimed mine soils. Near infrared spectroscopy (NIRS) with appropriate chemometric techniques could be used for OC and N-t monitoring in the mine soils. The aim of this study was to compare efficiency of NIR-based models developed using various chemometric approaches to predict OC and N-t contents in afforested mine soils. The studied approaches were: partial least square regression (PLSR), principal component regression (PCR) and artificial neural networks based on the entire spectral data (ANN), the principal components (PCA-ANN) and the latent variables (PLS-ANN) calculated from the spectral data. The samples (n = 90) of uppermost mine soil horizons (0-20 cm) were taken from the reclaimed dump of Belchatow lignite mine (Poland) and measured for the OC content by dry combustion and for the N-t content by Kjehldahl method. The samples were air-dried, finely ground and their NIR spectra (1000 nm-2500 nm) were recorded. The models were developed using 60 samples while the remaining 30 samples were used for independent validation. All the tested chemometric approaches (except the ANN model for OC) yielded excellent models useful for quantitative estimations. The PLSR models were promising at the calibration stage (values of ratio of inter-quartile distance to standard error of prediction (RPIQ(c)) were 7.00 and 4.22 for N-t and OC, respectively) however in the validation they performed less successfully (RPIQ(v) = 3.08 for N-t and RPIQ(v) = 2.75). The accuracy of ANN models based on the entire spectra was similar to PLSR or PCR models. However, the ANN based on reduced spectral data (PCA-ANN and PLS-ANN) performed distinctly better. The most accurate predictive models for the OC and N-t contents were obtained using PCA-ANN approach (RPIQ(v) = 3.64 and 2.90, for N-t and OC, respectively). The results indicate that NIRS coupled with ANN based on the reduced spectral data can be successfully applied to measure the OC and N-t contents in mine soils.
机译:需要快速分析方法来测量再生矿土壤中的有机碳(OC)和总氮(N-T)含量。具有适当化学计量技术的近红外光谱(NIRS)可用于矿井土壤中的OC和N-T监测。本研究的目的是比较利用各种化学计量方法开发的基于NIR的模型的效率,以预测植入矿井土壤中的OC和N-T含量。研究方法是:基于整个光谱数据(ANN),主要成分(PCA-ANN)和潜变量(PLS-ANN)的部分最小二乘回归(PLSR),主成分回归(PCR)和人工神经网络(PCA)(PLS-ANN)从光谱数据计算。从Belchatow Lignite Mine(波兰)的再生倾荷中取出最高矿井土程(0-20cm)的样品(n = 90),并通过kjehldahl方法测量OC含量和N-T含量。将样品风干,精细研磨,并记录其NIR光谱(1000nm-2500nm)。使用60个样本开发了模型,而剩余的30个样本用于独立验证。所有测试的化学计量方法(除OC的ANN模型除外)产生了优异的型号,可用于定量估计。 PLSR模型在校准阶段承诺(分别在预测的预测差异与标准误差的比例(RPIQ(C))分别为7.00和4.22),但在它们的验证中,它们较少执行(RPIQ (v)= 3.08对于NT和RPIQ(v)= 2.75)。基于整个光谱的ANN模型的准确性类似于PLSR或PCR模型。然而,基于降低的光谱数据(PCA-ANN和PLS-ANN)的ANN明显更好。使用PCA-ANN方法获得OC和N-T含量最准确的预测模型(RPIQ(v)= 3.64和2.90,分别为N-T和OC)。结果表明,基于降低的光谱数据耦合的NIR可以成功地应用于测量矿井土壤中的OC和N-T含量。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号