A review of artificial neural network based chemometrics applied in laser-induced breakdown spectroscopy analysis

• Published in: Spectrochimica acta. Part B: Atomic spectroscopy Vol. 180; p. 106183
• Main Authors: Li, Lu-Ning, Liu, Xiang-Feng, Yang, Fan, Xu, Wei-Ming, Wang, Jian-Yu, Shu, Rong
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier B.V 01.06.2021; Elsevier BV
• Subjects: Analysis; Analytical methods; Artificial neural network; Artificial neural networks; Back propagation networks; Chemometrics; Classification; Laser induced breakdown spectroscopy; Lasers; Machine learning; Neural networks; Radial basis function; Self organizing maps; Spectroscopic analysis; Spectroscopy; Spectrum analysis; Training; Artificial neural network; Chemometrics; Laser-induced breakdown spectroscopy; Machine learning; Classification
• ISSN: 0584-8547; 1873-3565
• DOI: 10.1016/j.sab.2021.106183

In the past decades various categories of chemometrics for laser-induced breakdown spectroscopy (LIBS) analysis have been developed, among which an important category is that based on artificial neural network (ANN). The most common ANN scheme employed in LIBS researches so far is back-propagation neural network (BPNN), while there are also several other kinds of neural networks appreciated by the LIBS community, including radial basis function neural network (RBFNN), convolutional neural network (CNN), self-organizing map (SOM), etc. In this paper, we introduce the principles of some representative ANN methods, and offer criticism on their features along with comparison between them. Then we afford an overview of ANN-based chemometrics applied in LIBS analysis, involving material identification/classification, component concentration quantification, and some unconventional applications as well. Furthermore, a comprehensive discussion on ANN-LIBS methodologies is provided from four aspects. First, a few general progressing trends are displayed. Next we expound some specific implementation techniques, including variable selection, network construction, data set utilization, network training, model evaluation, and chemometrics selection. In addition, the limitations of ANN approaches are remarked, mainly concerning overfitting and interpretability. Finally a prospect of future development of ANN-LIBS chemometrics is presented. Throughout the discussion quite a few good practices have been highlighted. This review is expected to shed light on the further upgrade of ANN-based LIBS chemometrics in the future. [Display omitted] •A review on artificial neural network (ANN) based LIBS chemometrics is provided.•Representative ANN algorithms are expounded and their applications in LIBS studies are presented.•The ANN-LIBS schemes are comprehensively discussed and good practices are extracted.