Abstract Most studies which have reported soil fertility attributes employing Energy Dispersive X-ray Fluorescence (EDXRF) combined with multivariate calibration make use of elemental concentration data. This combination may cause relevant information loss contained in EDXRF spectra. However, a well-established soil EDXRF spectra data treatment procedure for multivariate calibration is not currently available. The objective of this study was to evaluate the influence of different pre-processing and variable selection methods in partial least square regression models using EDXRF spectral data. Measurements were obtained under two experimental conditions (15 kV and 50 kV at tube) for soil organic carbon determination. Poisson scaling + mean center proved to be the most suitable pre-processing for this data set. The variable selection by successive projection algorithm for interval selection in partial least squares improved the performance of all tested pre-processing (or at least kept constant in terms of the errors). The 15 kV condition models with Pareto scaling and Poisson scaling + mean center were the most accurate and precise. The ratio to performance of deviation values for these models was of 2.2. The figures of merit demonstrated the soil organic carbon determination feasibility using EDXRF spectral data with these pre-processing since the accuracy, precision and limits of detection were consistent with previous reports. Thus, this study contributes toward the establishment of an approach for soil EDXRF spectral data treatment for multivariate calibration. It also contributes to a better EDXRF variables interpretation which impacts soil organic carbon modeling, demonstrating the proposed methodology potential.

中文翻译：

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基于偏最小二乘回归的能量色散X射线荧光光谱数据预处理和变量选择评价：以土壤有机碳预测为例

摘要 大多数报告土壤肥力属性的研究采用能量色散 X 射线荧光 (EDXRF) 结合多变量校准，利用元素浓度数据。这种组合可能会导致 EDXRF 光谱中包含的相关信息丢失。然而，目前还没有完善的用于多元校准的土壤 EDXRF 光谱数据处理程序。本研究的目的是使用 EDXRF 光谱数据评估偏最小二乘回归模型中不同预处理和变量选择方法的影响。在两种实验条件下（管电压为 15 kV 和 50 kV）获得测量值，用于测定土壤有机碳。Poisson scaling + mean center 被证明是最适合这个数据集的预处理。偏最小二乘中区间选择的连续投影算法的变量选择提高了所有测试预处理的性能（或至少在误差方面保持恒定）。具有帕累托标度和泊松标度 + 平均中心的 15 kV 条件模型最准确和精确。这些模型的偏差值与性能的比率为 2.2。由于准确度、精密度和检测限与之前的报告一致，品质因数证明了使用 EDXRF 光谱数据和这些预处理进行土壤有机碳测定的可行性。因此，本研究有助于建立一种用于多变量校准的土壤 EDXRF 光谱数据处理方法。