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首页> 外文期刊>Biosystems Engineering >Nitrate Determination in Soil Pastes using Attenuated Total Reflectance Mid-infrared Spectroscopy: Improved Accuracy via Soil Identification
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Nitrate Determination in Soil Pastes using Attenuated Total Reflectance Mid-infrared Spectroscopy: Improved Accuracy via Soil Identification

机译：衰减全反射中红外光谱法测定土壤糊中的硝酸盐：通过土壤鉴定提高准确性

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摘要

Direct determination of nitrate in soil is required for improving N-application management, which would help reduce soil and water pollution. Several works have demonstrated that mid-infrared Fourier transform infrared attenuated total reflectance (FTIR-ATR) spectroscopy could be used to determine nitrate concentration in soil pastes. The present work further investigates this approach, and proposes to combine nitrate determination with soil identification in order to improve the determination accuracy. The study focuses on soils commonly used for agriculture, which are classified according to soil taxonomy and their carbonate and clay contents. Soil identification is investigated using the 800-1200 cm{sup}(-1) and 1250-1550 cm{sup}(-1) intervals of the spectrum, using either cross-correlation with a reference library or principal component analysis (PCA) decomposition followed by neural network (NN) classifier. When applied to the 1250-1550 cm{sup}(-1) interval, the PCA-NN method leads to correct identification of all the samples, while the other approaches lead to poorer results. Nitrate determination is achieved using several partial least-squares regression models, each model being associated with a soil type. Determination errors range from 6.2 to 13.5mg[N]/kg[dry soil], depending on the soil type, with the lowest errors for light sandy soils. These determination errors are appreciably smaller than those obtained using a single model calibrated using all the data (19.1 mg[N]/kg[dry soil]).
机译：需要直接测定土壤中的硝酸盐以改善氮素施用管理，这将有助于减少土壤和水的污染。多项工作表明，中红外傅里叶变换红外衰减全反射光谱（FTIR-ATR）可用于确定土壤糊中的硝酸盐浓度。本工作进一步研究了这种方法，并建议将硝酸盐测定与土壤鉴定相结合，以提高测定准确性。这项研究集中在农业上常用的土壤上，这些土壤根据土壤分类法及其碳酸盐和粘土含量进行分类。使用与参考库的互相关或主成分分析（PCA），使用800-1200 cm {sup}（-1）和1250-1550 cm {sup}（-1）光谱间隔研究土壤鉴定分解，然后是神经网络（NN）分类器。当应用于1250-1550 cm {sup}（-1）的间隔时，PCA-NN方法可正确识别所有样本，而其他方法则导致较差的结果。使用几种偏最小二乘回归模型可以确定硝酸盐，每个模型都与土壤类型相关。根据土壤类型，测定误差范围为6.2至13.5mg [N] / kg [干土]，对于轻质沙质土壤，测定误差最低。这些测定误差明显小于使用所有数据校准的单个模型获得的测定误差（19.1 mg [N] / kg [干土]）。

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