Laser Induced Breakdown Spectroscopy

Laser-induced breakdown spectroscopy (LIBS) is an analytical technique used to determine the elemental composition of materials by analyzing the light emitted from a laser-induced plasma. Current research focuses on improving the accuracy and robustness of LIBS analysis, particularly through the application of advanced machine learning models such as convolutional recurrent neural networks, multitask learning architectures, and variational autoencoders to process the complex spectral data. These improvements aim to address challenges like limited training data, noise reduction, and the transferability of calibration models between different LIBS systems, ultimately enhancing the reliability and widespread applicability of LIBS in various fields.