Portable X-ray fluorescence (PXRF) measurements on 1520 soil samples were used to create national prediction models for copper (Cu), zinc (Zn), and cadmium (Cd) concentrations in agricultural soil. The models were validated at both national and farm scales. Multiple linear regression (MLR), random forest (RF), and multivariate adaptive regression spline (MARS) models were created and compared. National scale cross-validation of the models gave the following R2 values for predictions of Cu (R2 = 0.63), Zn (R2 = 0.92), and Cd (R2 = 0.70) concentrations. Independent validation at the farm scale revealed that Zn predictions were relatively successful regardless of the model used (R2 > 0.90), showing that a simple MLR model can be sufficient for certain predictions. However, predictions at the farm scale revealed that the non-linear models, especially MARS, were more accurate than MLR for Cu (R2 = 0.94) and Cd (R2 = 0.80). These results show that multivariate modelling can compensate for some of the shortcomings of the PXRF device (e.g., high limits of detection for certain elements and some elements not being directly measurable), making PXRF sensors capable of predicting elemental concentrations in soil at comparable levels of accuracy to conventional laboratory analyses.

中文翻译：

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使用便携式X射线荧光测量法预测土壤中的Cu，Zn和Cd浓度。

使用1520个土壤样品的便携式X射线荧光（PXRF）测量结果来创建关于农业土壤中铜（Cu），锌（Zn）和镉（Cd）浓度的国家预测模型。该模型在国家和农场规模上均得到了验证。创建并比较了多元线性回归（MLR），随机森林（RF）和多元自适应回归样条（MARS）模型。模型的国家尺度交叉验证为预测Cu（R2 = 0.63），Zn（R2 = 0.92）和Cd（R2 = 0.70）浓度提供了以下R2值。农场规模的独立验证表明，不管使用哪种模型，锌的预测都相对成功（R2> 0.90），这表明简单的MLR模型对于某些预测就足够了。但是，根据农场规模的预测，非线性模型 对于Cu（R2 = 0.94）和Cd（R2 = 0.80），特别是MARS比MLR更准确。这些结果表明，多变量建模可以弥补PXRF设备的某些缺点（例如，某些元素的检测限很高，并且某些元素无法直接测量），使得PXRF传感器能够以可比的水平预测土壤中的元素浓度。常规实验室分析的准确性。