Detection of soil organic matter from laser-induced breakdown spectroscopy (LIBS) and mid-infrared spectroscopy (FTIR-ATR) coupled with multivariate techniques
Detection of soil organic matter from laser-induced breakdown spectroscopy (LIBS) and mid-infrared spectroscopy (FTIR-ATR) coupled with multivariate techniques
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利用激光诱导击穿光谱 (LIBS) 和中红外光谱 (FTIR-ATR) 结合多元技术检测土壤有机质
DOI：
10.1016/j.geoderma.2019.113905
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发表时间：
2019-12
期刊：
影响因子：
6.1
通讯作者：
Zhou Jianmin
中科院分区：
文献类型：
--
作者：
Xu Xuebin;Du Changwen;Ma Fei;Shen Yazhen;Wu Ke;Laing Dong;Zhou Jianmin
光谱法是一种有用的土壤监测方法，因为它对环境友好，并且能够进行快速、无损、同时的多元素分析。在这项工作中，数据融合策略的激光诱导击穿光谱（LIBS）和衰减全反射傅里叶变换中红外光谱（FTIR-ATR），以及组合的多元校正方法进行了研究，用于预测土壤有机质（SOM）含量。应用校准和验证集的均方根误差（RMSE）和残差预测偏差（RPD）、系统误差和残差评估来评价这些预测的稳健性和准确性。主成分分析（PCA）的结果表明，基线漂移存在于光谱数据中，可以有效地消除形态加权惩罚最小二乘（MPLS）和小波变换（WT）算法。采用主成分加权平均法（PCWM）和欧氏距离加权平均法（EDWM）对平行LIBS光谱进行处理，可以提高偏最小二乘回归（PLSR）模型对SOM含量的定量预测能力。基于偏最小二乘算法获得的LIBS和FTIR-ATR光谱的潜在变量的级联的中间级数据融合的SOM含量的预测能力显着提高。PLSR模型具有较高的预测精度和鲁棒性（RV 2 = 0.792，RMSEV= 1.76 g kg−1，RPDV= 2.16），支持向量回归（SVR）模型（RV 2 = 0.811，RMSEV= 1.68 g kg−1，RPDV= 2.27）和人工神经网络（ANN）模型（RV 2 = 0.830，RMSEV= 1.60 g kg−1，RPDV= 2.39）。这项工作的结果表明，使用LIBS和FTIR-ATR光谱结合多元校正可以是一种简单，快速，无损的方法来监测SOM。该策略对土壤肥力评价、土壤养分管理以及指导精准农业的农业生产具有重要意义。
Spectroscopy is a useful method for soil monitoring because of its environmental friendliness, and its ability to produce rapid, nondestructive, simultaneous multi-element analysis. In this work, data fusion strategies for laser-induced breakdown spectroscopy (LIBS) and attenuated total reflectance Fourier-transform mid-infrared spectroscopy (FTIR-ATR), as well as a combination of multivariate calibration methods were investigated for prediction of soil organic matter (SOM) content. The root mean square error (RMSE) and residual prediction deviation (RPD) of the calibration and validation sets, systematic error, and residual assessment, were applied to evaluate the robustness and accuracy of these predictions. The results of a principal component analysis (PCA) indicated that baseline wander present in the spectral data could be effectively removed using morphological weighted penalized least squares (MPLS) and wavelet transform (WT) algorithms. The quantitative prediction ability of SOM content by a partial least squares regression (PLSR) model could be improved using principal component weighted mean (PCWM) and Euclidean distance weighted mean (EDWM) algorithms applied to parallel LIBS spectra. The prediction ability of SOM content was dramatically improved using mid-level data fusion based on the concatenation of latent variables of LIBS and FTIR-ATR spectra obtained by partial least squares algorithm. The considerable prediction accuracy and robustness were achieved using the PLSR model (RV2= 0.792, RMSEV= 1.76 g kg−1, and RPDV= 2.16), the support vector regression (SVR) model (RV2= 0.811, RMSEV= 1.68 g kg−1, and RPDV= 2.27), and the artificial neural network (ANN) model (RV2= 0.830, RMSEV= 1.60 g kg−1, and RPDV= 2.39). The findings from this work suggest that the use of LIBS and FTIR-ATR spectra in combination with multivariate calibration can be a simple, fast, and nondestructive approach to monitor SOM. This strategy is potentially of great significance in the evaluation of soil fertility, the management of soil nutrients, and in guiding the agricultural production of precision agriculture.
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影响因子：
3.5
作者：
D. H. Dieke
通讯作者：
D. H. Dieke
激光诱导击穿光谱定量分析中使用凸优化框架的新型基线校正方法
DOI：
10.1016/j.sab.2017.10.014
发表时间：
2017-12
期刊：
Spectrochimica Acta Part B: Atomic Spectroscopy
影响因子：
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影响因子：
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影响因子：
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