Identification of wood specimens utilizing fs-LIBS and machine learning techniques

Abstract

We report on the ability to identify wood specimens by utilizing 30 fs Laser Induced Breakdown Spectroscopy (LIBS) in conjunction with machine learning techniques. Ten different wood specimens have been studied. The spectral features were assigned to atomic/ionic and diatomic molecular transitions. The origin of the latter has been explored by investigating the dynamics of the created plume in ambient and argon atmosphere. Principal Component Analysis (PCA) was employed for dimensionality reduction based on the primary LIBS analysis. The principal components formation is grounded on the CN, Ca II, Ca I, and Na, LIBS data. Furthermore, applying the weighted k nearest neighbor (kNN) algorithm led to an accurate identification of the investigated specimens, since the evaluation metrics of specificity value were found to be in the range of 0.96–1.00, while that of accuracy was within 0.93–1.00.