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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.<\/jats:p>","DOI":"10.3233\/jifs-171979","type":"journal-article","created":{"date-parts":[[2018,11,2]],"date-time":"2018-11-02T12:08:05Z","timestamp":1541160485000},"page":"6277-6286","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":10,"title":["Quantitative analysis of LIBS spectra using hybrid chemometric models through fusion of extreme learning machines and support vector regression"],"prefix":"10.1177","volume":"35","author":[{"given":"Taoreed O.","family":"Owolabi","sequence":"first","affiliation":[{"name":"Department of Physics, Laser Research Group, Center of Excellence in Nanotechnology King Fahd University of Petroleum &amp; Minerals, Dhahran, Saudi Arabia"}]},{"given":"Mohammed 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