Classification of plastics using laser-induced breakdown spectroscopy combined with principal component analysis and K nearest neighbor algorithm

Using K-nearest neighbor(kNN) and principal component analysis(PCA) algorithm to support multi-target classification. The loading plot of each principal component and the relationship between principal component and classification accuracy are provided. On the premise of reducing the data dimension to 20 dimensions, the training time of classification model based on 1000 sample data is reduced from 368.99 seconds to 167.30 seconds, while the time for individual sample classification is reduced from about 0.11 seconds to about 0.02 seconds, and the classification accuracy reaches 99.6%.