Improvement of the performances of a commercial hand-held laser-induced breakdown spectroscopy instrument for steel analysis using multiple artificial neural networks

Comparison of Convolutional and Conventional Artificial Neural Networks for Laser-Induced Breakdown Spectroscopy Quantitative Analysis

The introduction of "deep learning" algorithms for feature identification in digital imaging has paved the way for artificial intelligence applications that up to a decade ago were considered technologically impossible to achieve, from the development of driverless vehicles to the fully automated diagnostics of cancer and other diseases from histological images. The success of deep learning applications has, in turn, attracted the attention of several researchers for the possible use of these methods in chemometrics, applied to the analysis of complex phenomena as, for example, the optical emission of laser-induced plasmas. In this paper, we will discuss the advantages and disadvantages of convolutional neural networks, one of the most diffused deep learning techniques, in laser-induced breakdown spectroscopy (LIBS) applications (classification and quantitative analysis), to understand the real potential of "deep LIBS" in practical everyday use. In particular, the comparison with the results obtained using "shallow" artificial neural networks will be presented and discussed, taking as a case study the analysis of six bronze samples of known composition.

Quantitative Analysis of Pig Iron from Steel Industry by Handheld Laser-Induced Breakdown Spectroscopy and Partial Least Square (PLS) Algorithm

One of the main objectives in the steel production process is to obtain a blast furnace pig iron of good quality and at the lowest possible cost. In general, the quality of pig iron is evaluated on the basis of its chemical composition determined by X-ray fluorescence laboratory equipment. In the present study, the performance of a handheld (h) laser-induced breakdown spectroscopy (LIBS) instrument in the identification and the quantification of the relevant elements C, Mn, P, Si, and Ti in forty-six blast furnace pig iron samples was tested successfully. The application of two different models, i.e., univariate and multivariate partial least square (PLS) calibration and validation, to the whole LIBS data set showed that the latter approach was much more efficient than the former one in quantifying all elements considered, especially Si and Ti.