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The spatial distribution characteristics of iron ore components are important when measuring the difficulty of their beneficiation. Polarized light microscopy and scanning electron microscopy are traditional methods with some shortcomings, including complicated operation and low efficiency. Most of the laboratory hyperspectral imaging techniques that have emerged in recent years have been focused on the field of mineral resource exploration. In contrast, the mineral distribution and tectonic characteristics of iron ores have been relatively poorly studied in the field of beneficiation. To address the issue, 11 experimental samples of banded iron formation (BIF)-hosted iron ores were selected and tested using an imaging spectrometer. Then, based on the differences in spectral characteristic of the three main components (quartz, hematite, and magnetite) in the samples, the identification model of the spatial distribution of the iron ore components was established using the normalized spectral amplitude index (NSAI) and spectral angle mapper (SAM). The NSAI and SAM identify minerals based on spectral amplitude features and spectral morphological features of the sample, respectively. The spatial distribution of different minerals in the samples was tested using the model, and the test results demonstrated that the spatial distribution of the three components is consistent with the banded tectonic character of the sample. Upon comparison with the chemical test results, the mean absolute errors (MAE) of the model for quartz, hematite, and magnetite in the samples were 2.03%, 1.34%, and 1.55%, respectively, and the root mean square errors (RMSE) were 2.72%, 2.08%, and 1.85%, respectively, with the exception of one martite sample that reached an MAE of 10.17%. Therefore, the model demonstrates a high degree of accuracy. The research provides a new method to test the spatial distribution of iron ore components.
Yi et al. (Mon,) studied this question.