Abstract Assessment of mineral liberation spectrum with all its aspects is essential for plant control and optimization. This paper aims to estimate 2D mineral map and its associated liberation spectrum of a particular chromite sample from optical micrographs by using Random Forest Classification, a powerful machine-learning algorithm implemented on a user-friendly and an open-source software. This supervised classification method can be used to accurately generate 2D mineral map of this chromite sample. The variation of the measured spectra with the sample size is studied, showing that images of 200 particles randomly selected from the optical micrographs are sufficient to reproduce liberation spectrum of this sample. In addition, the 2D spectrum obtained with this classification method is compared with the one obtained from the Mineral Liberation Analyzer (MLA). Although 2D mineralogical compositions obtained by the two methods are quite similar, microscopic analysis estimates poorer liberation than MLA due to the residual noise (misclassified gangue) generated by the classification. Nevertheless, we cannot compare the reliabilities of the two methods, as there is not a standard produce yet to quantify the accuracy of MLA analysis.

中文翻译：

---

通过监督图像分类评估显微图像上的铬铁矿释放谱

摘要 矿物释放谱及其各个方面的评估对于植物控制和优化至关重要。本文旨在通过使用随机森林分类（一种在用户友好的开源软件上实现的强大机器学习算法）从光学显微照片中估计特定铬铁矿样品的 2D 矿物图及其相关的解放光谱。这种监督分类方法可用于准确生成该铬铁矿样品的二维矿物图。研究了测量光谱随样品尺寸的变化，表明从光学显微照片中随机选择的 200 个粒子的图像足以再现该样品的释放光谱。此外，将使用这种分类方法获得的二维光谱与从矿物解离分析仪 (MLA) 获得的光谱进行比较。尽管通过两种方法获得的 2D 矿物成分非常相似，但由于分类产生的残余噪声（错误分类的脉石），微观分析估计比 MLA 更差的释放。然而，我们无法比较这两种方法的可靠性，因为还没有一个标准产品来量化 MLA 分析的准确性。