Mining heavy metal ore deposits may lead to an increase in heavy metal element content in surrounding soils, which could pose irreversible harm to the ecological environment and human health. Therefore, analyzing and classifying soils from different mining areas is of great significance and can provide reference for soil management and environmental pollution control. Laser-induced breakdown spectroscopy (LIBS) has gradually become a research hotspot in soil detection due to its fast and pre-treatment-free characteristics. However, traditional LIBS technology has problems such as low sensitivity, high noise, and poor repeatability, which affect its accuracy. Therefore, this paper proposes a soil classification method based on Principal Component Analysis (PCA) of LIBS technology coupled with K-Nearest Neighbor algorithm (KNN). This method first conducts data standardization and PCA pre-processing to eliminate redundant information and improve signal-to-noise ratio. Then, autonomous sampling technology is used to design the KNN machine learning algorithm structure to generate continuous analytical networks for training and testing sets. Finally, the results show that the soil classification accuracy of the PCA-KNN machine learning model can reach 97.531%, proving that the combination of LIBS technology and PCA-KNN can achieve rapid and accurate classification of soils from different mining areas. Therefore, this method has the significance of providing new ideas and methods for soil classification in different regions.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.