Quantitative analysis of LIBS spectra using hybrid chemometric models through fusion of extreme learning machines and support vector regression

Abstract

Laser induced breakdown spectroscopy (LIBS) is an excellent technique for analysis of solid and liquid samples. However there are inherent problems with concentration determination of elements present in the test sample with better accuracy. In order to address this challenge, hybrid fusion of extreme learning machine (ELM) and support vector regression (SVR) is proposed for the first time. Extreme learning machine (ELM) is a non-linear chemo-metric method which has inherent capacity to approximate any non-linear relation describing the laser induced plasma. However, ELM surfers from over-fitting which affects its accuracy for spectroscopic regression. On the other hand, SVR is a non-linear chemo-metric tool based on statistical learning theory and overcomes the problem of over-fitting by proper tuning of its hyper-parameters. The merits of both chemo-metrics are harnessed in this work and implemented for quantitative analysis of LIBS spectra of seven standard bronze samples. The performance of ELM-SVR model which uses the output of ELM as its input is compared to that of SVR-ELM model which takes the output of SVR as its input. The hyper-parameters of the proposed models are optimized using gravitational search algorithm (GSA). On the bases of root mean square error (RMSE) as a measure of model performance, ELM-SVR performs better than SVR, ELM and SVR-ELM model with performance improvement of 95.76%, 89.33% and 52.71%, respectively. The accuracy of the proposed hybrid models would be of immense significance for quick quantitative analysis in LIBS and eventually promotes wide applicability of the technique.

Keywords

LIBS spectra extreme learning machine gravitational search algorithm support vector regression quantitative analysis hybrid model

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