Determination of La in rare earth ores using laser-induced breakdown spectroscopy combined with bidirectional long short-term memory

The detection of lanthanum (La) is crucial for the extraction of rare earth minerals. As a real-time, in-situ detection technique, laser-induced breakdown spectroscopy (LIBS) is adversely affected by severe matrix effects, leading to the poor performance of univariate analysis in La analysis in geological samples. Here, a multivariate regression model based on bidirectional long short-term memory (BiLSTM) neural network integrated with LIBS (BiLSTM-LIBS) is proposed to enhance the quantitative analysis of La in rare earth minerals. To demonstrate the superiority of this approach, we compare the results with traditional univariate analysis techniques. The linear correlation coefficient is increased from about 0.969 to 0.999 by the BiLSTM-LIBS model, while the RMSEC is reduced from about 1.294 to 0.491 ppk. The mean relative error for test data is 5.85%, and the root means square error of prediction is 0.397. These results indicate that the combination of LIBS technology and BiLSTM neural network effectively reduces the impact of matrix effects, providing an available route for accurate determination of La in rare earth minerals.