Determination of soil properties with visible to near- and mid-infrared spectroscopy: Effects of spectral variable selection
Determination of soil properties with visible to near- and mid-infrared spectroscopy: Effects of spectral variable selection
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DOI：
10.1016/j.geoderma.2014.01.013
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发表时间：
2014-07-01
期刊：
影响因子：
6.1
通讯作者：
Ludwig, B.
中科院分区：
文献类型：
--
作者：
Vohland, M.;Ludwig, M.;Ludwig, B.
光谱变量选择是光谱数据分析中的一个重要步骤，因为它倾向于简化数据表示，并且可以产生具有更强预测能力的多变量模型。在这项研究中，我们使用VIS-NIR（可见到近红外）漫反射和DRIFT（中红外漫反射红外傅立叶变换，MIR）光谱来确定土壤的一系列化学和生物特性。使用全吸光度光谱(VIS-NIR: 400-2500 nm，间隔5 nm； MIR: 4000-800 cm（-1)，间隔4 cm(-1)）以及PLSR和CARS（竞争自适应再加权采样）的组合进行多变量校准，以仅整合最具信息的关键变量。CARS方法尚未在土壤光谱学领域得到应用。由于集合异质性对于最佳校准至关重要，我们对60个农业样本进行了测试，这些样本涵盖了广泛的不同母质、土壤质地、有机质含量和土壤pH值。在进行实验室光谱测量之前，从Ap层（0-10厘米深）采集土壤样品，风干并粉碎。在交叉验证方法中，对于所有研究的土壤变量和两个光谱区域，CARS-PLSR方法明显比全光谱- plsr方法更准确。利用MIR数据和CARS-PLSR，有机碳（OC）、氮(N)、微生物生物量c （C-mic）和pH值的残差预测偏差（RPD）大于3.0，得到了很好的结果；热水可提C （C-hwe）的RPD为2.60。VIS-NIR数据获得的精度明显低于MIR光谱；pH和C-mic的最佳结果（RPD值在2.0和2.5之间表示近似定量）。二维相关分析表明，MIR数据的信息含量与VIS-NIR信息有很大不同。我们发现，两个光谱区域之间的二维相关模式总体上是模糊的，相关系数为中低，这表明所研究的土壤样品种群的异质性导致了近红外区域泛音和组合带的非常复杂的模糊。统计CARS选择在物理上是合理的。所研究C组分的MIR关键波数在2920 cm（-1）和2850 cm（-1）波段（均为脂肪族ch基团）和1740 ~ 1600 cm（-1）波段（co基团），分别代表土壤有机质的疏水和亲水化合物。主要的可见光-近红外波长位于1915 nm和2200 nm的显著吸水带附近。方法的简单性、多元模型的简便性、交叉验证的准确性和物理上合理的选择表明CARS程序的成功运行。应使用单独的校准和验证集，用更多的样品进一步检查。(C) 2014 Elsevier B.V.版权所有
Spectral variable selection is an important step in spectroscopic data analysis, as it tends to parsimonious data representation and can result in multivariate models with greater predictive ability. In this study, we used VIS-NIR (visible to near-infrared) diffuse reflectance and DRIFT (diffuse reflectance infrared Fourier transform in the mid-infrared range, MIR) spectroscopy to determine a series of chemical and biological soil properties. Multivariate calibrations were performed with partial least squares regression (PLSR) using the full absorbance spectra (VIS-NIR: 400-2500 nm with 5-nm intervals; MIR: 4000-800 cm(-1) with 4-cm(-1) intervals) and with a combination of PLSR and CARS (competitive adaptive reweighted sampling) to integrate only the most informative key variables. The CARS procedure has as yet not been applied in the field of soil spectroscopy. As set heterogeneity is crucial for an optimal calibration, we tested these approaches to a sample set of 60 agricultural samples covering a broad range of different parent materials, soil textures, organic matter contents and soil pH values. Soil samples were taken from the Ap horizon (0-10 cm depth), air-dried and pulverised before the lab spectroscopic measurements were performed. In a cross-validation approach, the CARS-PLSR method was markedly more accurate than full spectrum-PLSR for all investigated soil variables and both spectral regions. With MIR data and CARS-PLSR, excellent results (indicated by a residual prediction deviation (RPD) greater than 3.0) were obtained for organic carbon (OC), nitrogen (N), microbial biomass-C (C-mic) and pH values; for hot water extractable C (C-hwe), RPD was 2.60. The accuracies obtained with VIS-NIR data were considerably lower than those with the MIR spectra; best results were retrieved for pH and C-mic (approximately quantitative as indicated by RPD values between 2.0 and 2.5). The information content of the MIR data was substantially different from the VIS-NIR information, as indicated by 2D correlation analysis. We found an overall blurred 2D correlation pattern between both spectral regions with moderate to low correlation coefficients, which suggested that the heterogeneity of the studied soil sample population had led to a very complex blurring of overtones and combination bands in the NIR region.Statistical CARS selections were physically reasonable. MIR key wavenumbers for the studied C fractions were inter alia identified at the bands at 2920 cm(-1) and 2850 cm(-1) (both aliphatic CH-groups) and the region between 1740 and 1600 cm(-1) (CO-groups) and represent hydrophobic and hydrophilic compounds of soil organic matter. Important VIS-NIR wavelengths for assessing C fractions and N were located nearby the prominent water absorption band at 1915 nm and the hydroxyl band at 2200 nm.The simplicity of the approach, parsimony of the multivariate models, accuracy levels in the cross-validation and physically reasonable selections indicated a successful operation of the CARS procedure. It should be further examined with a larger number of samples using separate calibration and validation sets. (C) 2014 Elsevier B.V. All rights reserved.