Geochemical Data from Iron Ore Exploration, Western Minas Gerais, Brazil

This dataset comprises a lithogeochemical database generated from an iron ore deposit located in the western portion of the state of Minas Gerais, Brazil. The region is well known for iron ore exploration because it hosts numerous small- to medium-sized high-grade iron ore bodies, representing an important metallogenic district within the Brazilian iron ore province. The database was developed to support lithological classification based on the integration of geological logging and whole-rock geochemistry. Lithological classification was established using macroscopic descriptions of drill core, visual assessment of primary structures and textures, and multi-element geochemical analyses obtained from 22 diamond drill holes (DDH). The deposit was classified into seven principal lithotypes: Ferruginous colluvium, Itabirite, High-grade itabirite, AmphiboliteFerruginous amphibolite, Granite and Gneiss A total of 646 drill core samples were analyzed for whole-rock geochemistry. Chemical analyses were performed by ALS Chemex (Lima, Peru) following standardized quality-controlled analytical procedures. Major and minor elements were determined using the ME-XRF21u analytical package, based on X-ray fluorescence (XRF) fusion spectroscopy, which is specifically optimized for iron-rich geological materials. The principal analytes include FeO, Al₂O₃, As, Ba, CaO, Cl, Co, Cr₂O₃, Cu, Fe, K₂O, MgO, Mn, Na₂O, Ni, P, Pb, S, SiO₂, Sn, Sr, TiO₂, V, Zn, and Zr. Analytical detection limits ranged from 0.001 to 0.01 wt.%, depending on the element. Loss on ignition (LOI; analytical code OA-GRA05x) was determined gravimetrically after furnace ignition at 1000°C. Ferrous iron (FeO) concentrations were measured by volumetric titration (analytical code Fe-VOL05) following acid digestion using a sulfuric acid–hydrofluoric acid (H₂SO₄–HF) mixture. The dataset was compiled to support research in lithological classification, machine learning, mineral exploration, applied geochemistry, and mineral resource characterization. It provides a high-quality benchmark for supervised classification methods, statistical analyses, and multivariate data exploration in iron ore systems.