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The development of heavy metal mining area will pollute the surrounding soil and do great harm to the ecological environment and human health. Soil classification in different mining areas is of great significance to soil management and environmental pollution control. Soil is easily affected by matrix effect due to its complex physical properties and composition. Thus, accurately classifying soils is a challenge. Laser-induced breakdown spectroscopy has developed rapidly in the past two decades. It has been widely used in the detection of various physical samples due to its characteristics of fast analysis speed and no need for sample pretreatment. However, traditional LIBS technology has disadvantages such as low sensitivity, obvious noise and poor repeatability, which affect the accuracy of quantitative analysis. In this paper, a soil classification method based on principal component analysis (PCA) based laser-induced breakdown spectroscopy (LIBS) and random forest (RF) algorithm was proposed, and the standard soil samples from six different mining areas were accurately identified and classified. The final prediction results based on this combination show that the accuracy of soil classification by PCA-RF machine learning model can reach 97.86%. From the aspect of classification accuracy, it can be found that laser-induced breakdown spectroscopy combined with PCA-RF can achieve rapid and accurate classification of soil in different mining areas, which also provides a new method for soil classification in heavy metal mining areas.
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