Laser-Induced Breakdown Spectroscopy and a Convolutional Neural Network Model for Predicting Total Iron Content in Iron Ores

Laser-induced breakdown spectroscopy (LIBS) is a rapid method for detecting total iron (TFe) content in iron ores. However, accuracy and measurement error of univariate regression analysis in LIBS are limited due to factors such as laser energy fluctuations and spectral interference. To address this, multiple regression analysis and feature selection/extraction are needed to reduce redundant information, decrease the correlation between variables, and quantify the TFe content of iron ores accurately. Overall, 339 batches of iron ore samples from five countries were obtained from the ports of China during the discharging, and 2034 representative spectra were collected. A convolutional neural network (CNN) model for total iron content prediction in iron ores is established. The performance of variable importance random forest (VI-RF), variable importance back propagation artificial neural network (VI-BP-ANN), and CNN-assisted LIBS in predicting the TFe content of iron ores was compared. Coefficient of determination (