Ore texture and mineralogy exert a dominant control on breakage behaviour, liberation, and intrinsic mechanical properties during comminution; however, extracting texture-related information in a form suitable for geometallurgical studies remains challenging. This study investigates the potential of P-wave ultrasonic velocity as a non-destructive proxy of comminution-relevant ore properties. Samples from porphyry copper, orogenic gold, and iron oxide-apatite deposits were characterised using ultrasonic pulse velocity measurements in combination with modal mineralogy, textural analysis, rebound hardness, and small-scale comminution testing. Multivariate statistical analysis reveals systematic relationships between P-wave velocity and intrinsic ore properties, reflecting variations in mineralogy, texture, and mechanical response. Microwave-treated samples further demonstrate that P-wave measurements are sensitive to defect generation and microstructural modification. These results indicate that P-wave ultrasonic velocity provides a promising, non-destructive indicator of ore characteristics relevant to comminution performance, with potential applications in geometallurgical characterisation and ore variability assessment.

As the geometallurgy concept gains more visibility, the importance of parameters, in particular, ore texture, in downstream processing performance is increasingly recognized, yet methodologies for fast, unbiased, and automated texture classification remain limited, particularly for complex and low-grade deposits. This study proposes an alternative approach with potential for automated ore texture classification by combining micro-X-ray fluorescence (μ-XRF) imaging with unsupervised machine learning. Drill core samples from northern Sweden iron ore deposits were analyzed using μ-XRF to produce high-resolution mineral and X-ray intensity maps, which were converted to grayscale, divided into patches, and processed with Gray Level Co-Occurrence Matrix (GLCM) for feature extraction. Additionally, Principal Component Analysis (PCA) was applied for dimensionality reduction and k-means clustering for textural classification. The integration of mineral and X-ray maps improved classification accuracy, with clustering results effectively distinguishing major textural groups, despite some misclassifications attributed to pixel intensity variations. Evaluation of possible correlation between the classified textures and Leeb hardness measurements was carried out. Promising results were obtained, however, future advancements, such as the application of deep learning and alternative clustering algorithms, could further enhance the accuracy and applicability of this technique.

Understanding ore texture is essential for optimising comminution and mineral liberation, yet no standardised framework exists for defining and quantifying textural attributes. This review explores the complex relationship between ore texture, breakage mechanisms, and mineral liberation, emphasising the importance of textural parameters such as grain size, mineral intergrowths, and mechanical properties in determining comminution behaviour. The review is structured into key themes: (i) characterisation techniques for ore texture, spanning optical, electron beam, X-ray, and laser-based methods; (ii) conventional and proxy comminution tests for assessing ore hardness and breakage response; (iii) modelling approaches to relate ore texture to mineral liberation, including texture-based and kinetic-based liberation models; and (iv) innovative pre-treatment methods such as microwave heating, high-voltage pulse disintegration, and ultrasonication, which aim to promote non-random breakage and improve mineral liberation. The literature is analysed through a comparative assessment of these methods, evaluating their applicability, limitations, and integration potential in modern mineral processing.

Key takeaways include the need for a standardised classification of ore texture, at least for mineral processing purposes, improved methods for quantifying breakage modes, and the development of more accurate liberation models that incorporate textural heterogeneity. While advanced pre-treatment techniques show promise in promoting non-random comminution, their widespread adoption remains constrained by energy consumption and economic feasibility. By synthesising recent advancements and identifying research gaps, this review contributes to the field by advocating for a geometallurgical approach that integrates ore texture characterization into comminution models. 

Traditionally, geochemical assays have been used in geometallurgical programs to determine grade and recovery of the ore. The efficiency of this approach is questionable, since assays i) provide bulk geochemistry without providing host mineralogy for the element(s) of interest, and ii) are performed on small samples. Thus, ore mineral grade might be lower than the assays imply, due to the inclusion of elements of interest in the gangue mineralogy. Also, the samples analysed might not be representative on a deposit size scale due to their small volume. In μ-XRF, areas analysed are on a dm scale, providing a larger, and therefore more representable, analysis than e.g. a scanning electron microscope (SEM), yet providing a resolution comparable to SEM analyses. Another advantage of the μ-XRF is the possibility to detect elements as light as sodium, while simultaneously detecting heavy elements, e.g. REEs.Here, cut drillcore samples were scanned by μ-XRF at varying resolutions. The μ-XRF data was utilised for i) comparison with chemical assays, ii) identification of sample mineralogy, iii) comparison with mineralogy from X-ray diffraction (XRD), and iv) analysis of ore texture. The results show that regardless of the resolution used, the μ-XRF analyses correlate well with the results from geochemical assays, whereas for textural features a finer resolution yielded a more detailed picture, as was expected. The drillcore mineralogy compares well with the phases identified by XRD. However, mineral identification from μ-XRF is based on elemental spectrums. Therefore, polymorphs cannot be successfully discriminated and an insight into the deposit mineralogy, is needed for a correct mineral classification in these cases.