There is a growing interest in the use of soil composition as a form of evidence in food provenance, forensics, biosecurity, and archaeology. Given a soil sample of unknown origin, we should like to know the likely geographical source of that material. In this study, we investigated whether data provided from a rapid and non-destructive sensor can be used to identify the provenance of a soil sample. A portable X-ray fluorescence (pXRF) spectrometer was used to measure the elemental abundance of 0–10 cm soil samples from a part of the Lower Hunter Valley, NSW, Australia (an area of 328 km²). Three methods, namely, two similarity methods (points of similarity and regions of similarity) based on distances to the unlocated specimen in the principal component (PC) space of the geochemical data, and an artificial neural network (ANN) method, effectively an inverse digital soil mapping (DSM) approach, which predicts location from the set of geochemical variables, were tested to determine the provenance of soil samples. In the PC approach, digital soil maps of the PC scores of eight major elements and two elemental ratios were created. The locations predicted by the PC approach seemed to follow the pattern of topography. In the ANN approach, the geographical coordinates (Eastings and Northings) of a sample were predicted simultaneously using the elemental concentrations and ratios. Using maps of elemental concentration classes (regions of similarity based on PC) provided a mean RMSE of 8.6 km for the 147 validation samples. The different effects of identification of geographical locations were compared using a 95% spatial confidence interval of prediction on a validation dataset. The points of similarity based on PC approach showed that the predicted search areas can capture 59% of the true locations of the test data. Meanwhile, the ANN approach can capture 69% of the true locations of the.

data. The mean RMSE for ANN prediction (2.8 km) was smaller than that for points of similarity prediction (4.3 km). Both soil provenancing approaches are potentially useful in identifying geographical areas of origin or similarity.

人们越来越关注使用土壤成分作为食品来源，法医学，生物安全和考古学的证据形式。给定未知来源的土壤样本，我们想知道该物质的可能地理来源。在这项研究中，我们调查了从快速无损传感器提供的数据是否可用于识别土壤样品的来源。便携式X射线荧光（pXRF）光谱仪用于测量澳大利亚新南威尔士州下亨特河谷一部分地区（面积328 km ^2） 0-10 cm土壤样品的元素丰度）。三种方法，即两种基于地球化学数据主成分（PC）空间中未定位标本的距离的相似性方法（相似性点和相似性区域）和一种人工神经网络（ANN）方法，可以有效地进行逆测试了数字土壤测绘（DSM）方法，该方法可从一组地球化学变量预测位置，从而确定土壤样品的来源。在PC方法中，创建了八个主要元素和两个元素比率的PC分数的数字土壤图。PC方法预测的位置似乎遵循地形模式。在人工神经网络方法中，使用元素浓度和比例同时预测样品的地理坐标（东和北）。使用元素浓度分类图（基于PC的相似区域），对147个验证样本的平均RMSE为8.6 km。使用验证数据集上95％的预测空间置信区间比较了地理位置识别的不同影响。基于PC方法的相似点表明，预测的搜索区域可以捕获测试数据真实位置的59％。同时，ANN方法可以捕获69％的真实位置。基于PC方法的相似点表明，预测的搜索区域可以捕获测试数据真实位置的59％。同时，ANN方法可以捕获69％的真实位置。基于PC方法的相似点表明，预测的搜索区域可以捕获测试数据真实位置的59％。同时，ANN方法可以捕获69％的真实位置。

数据。ANN预测的平均RMSE（2.8 km）小于相似点预测的平均RMSE（4.3 km）。两种土壤出处方法在识别起源或相似的地理区域方面都可能有用。