Parent material (PM) is key in the thorough understanding of soils. However, the complexity of PM distributions and the difficulty of reaching PM in deep soils prevent its detailed assessment. Proximal sensors, such as the portable X-ray fluorescence spectrometer (pXRF), might ease this process. This work attempts to prove the potential of pXRF to predict different PMs from analyses of soil samples. The study encompassed five Brazilian states representing 1,541,309.409 km², from where 310 soil samples of various soil classes derived from 12 different PMs were collected and analyzed by PXRF. Support Vector Machine (SVM) and Random Forest (RF) algorithms were used for modeling. Modeling comprised three datasets: one containing all data (310 samples), a dataset with younger soils (151 samples) and one with older soils, conceptually less influenced by their PM (159 samples), to understand how soil-PM chemical proximity affects prediction performance, assessed via overall accuracy and Kappa coefficient. Data distribution showed pXRF can discriminate PM types via their resulting soils, regardless of the degree of weathering. Prediction results were prominent: RF and SVM achieved roughly 0.9 Kappa and overall accuracy predicting all data. For the remaining datasets, SVM achieved 0.96 Kappa and RF nearly 0.92 for younger soils, and 0.87 and 0.9, respectively, for older soils, confirming that PMs of younger soils are slightly easier to predict, but even soils heavily altered by pedogenetic processes can be accurately predicted. Results confirm the pXRF potential to predict PM from soil data, which might help in soil mapping and its consequent activities in tropical conditions.

母体材料（PM）是全面了解土壤的关键。但是，深层土壤中PM分布的复杂性和难以达到PM的情况阻碍了对其的详细评估。近端传感器（例如便携式X射线荧光光谱仪（pXRF））可能会简化此过程。这项工作试图证明pXRF从土壤样品分析中预测不同PM的潜力。该研究涵盖了代表1,541,309.409 km ^2的五个巴西州，从中收集了来自12种不同PM的310种不同土壤类别的土壤样品，并进行了PXRF分析。支持向量机（SVM）和随机森林（RF）算法用于建模。建模包含三个数据集：一个包含所有数据（310个样本），一个具有较新土壤的数据集（151个样本）和一个具有较旧土壤的数据集（概念上受其PM影响较小（159个样本）），以了解土壤-PM化学邻近性如何影响预测性能，通过总体准确性和Kappa系数进行评估。数据分布表明，无论风化程度如何，pXRF均可通过其产生的土壤区分PM类型。预测结果非常突出：RF和SVM大约达到0.9 Kappa，并且预测所有数据的整体准确性。对于其余的数据集，SVM达到0.96 Kappa，RF接近0。较年轻的土壤为92，较旧的土壤分别为0.87和0.9，这证实较年轻的土壤的PM较容易预测，但即使是由成岩作用严重改变的土壤也可以准确预测。结果证实了pXRF从土壤数据预测PM的潜力，这可能有助于土壤作图及其在热带条件下的活动。