Identification of metals and alloys using color CCD images of laser-induced breakdown emissions coupled with machine learning

We demonstrate here, for the first time to the best of our knowledge, the method of classification and identification of metals, metal alloys using the color CCD images of femtosecond (fs) laser-induced plasma emissions. The non-gated color CCD images of the plasma emissions were used to train the machine learning algorithm for identification. We have also compared the obtained results with the fs-laser-induced breakdown spectroscopy (LIBS) results. The green channel in the RGB image was used for the classification and prediction of metals and metal alloys. The present work explores the possibility of identification of the aluminum, copper, bronze, and steel using a simple instrument such as the CCD. Each sample formed extended clusters in the classification performed using principal component analysis (PCA). The extracted features from the PCA were used as input to train the support vector machine (SVM) and for prediction and the results are intriguing.