Transferred multitask regularization convolutional neural network (TrMR-CNN) for laser-induced breakdown spectroscopy quantitative analysis

TL;DR: This work presents the first review of DL approaches in LIBS spectra analysis, where the principles and applications are introduced and summarized, and demonstrates that DL exhibits great potential and a promising future inLIBS analysis.

TL;DR: A hybrid transfer learning method (HTr-LIBS) is proposed to further enhance the performance of LIBS coal analysis by combining fine-tuning with sample reweighting and significantly improved the analytical accuracy compared to direct modeling using small training sets.

TL;DR: Rapidly quantify raw rocks using LIBS coupled with feature-based transfer learning to overcome challenges associated with spectral differences between pressed pellet and rock samples.

TL;DR: Recent advances in machine learning methodologies for LIBS quantitative analysis focus on improving the accuracy and reliability of quantitative results by addressing challenges such as experimental conditions, sample surface state, and matrix effect.

TL;DR: An end-to-end deep learning approach incorporated Inception module, named DeepSpectra, is presented to learn patterns from raw data to improve the model performance and shows improved results than conventional linear and nonlinear calibration approaches in most scenarios.

TL;DR: Methods for raw signal improvement including sample preparation, system optimization, and especially plasma modulation, which modulates the laser-induced plasma evolution process for higher signal repeatability and signal-to-noise ratio, were reviewed and discussed.

TL;DR: In this paper, the authors present a comprehensive review of the use of laser-induced breakdown spectroscopy (LIBS) for coal analysis, including fundamentals and key factors, operation modes, data processing and analytical results.

TL;DR: The strong performance of the sparse methods in this study suggests that use of dimensionality-reduction techniques as a preprocessing step may improve the performance of both the linear and nonlinear models.