Since its commercialization in the 1950s, X-ray fluorescence (XRF) spectrometry has become the dominant elemental technique of choice for determining low parts-per-million (ppm) to percentage concentration levels in solid samples, including metals, alloys, ores, rocks, soils . Since its commercialization in the 1950s, X-ray fluorescence (XRF) spectrometry has become the dominant elemental technique of choice for determining low parts-per-million (ppm) to percentage concentration levels in solid samples, including metals, alloys, ores, rocks, soils . In this context, this research aimed to compare the performance of different predictive models, including a simple linear regression (RS), multiple linear regression (MLR), partial least-squares regression (PLS), and random forest (RF) models for the prediction of Ca and K in agricultural soils. RS. X-ray fluorescence is a non-destructive analytical technique used to determine a material's elemental composition. Major, minor and trace elements can be qualitatively and quantitatively determined in various kinds of samples: metals, alloys, glasses, cements, minerals, rocks, ores, polymers s well as environmental and biological materials.
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