Retracted Article: Novel techniques for enhancing the performance of support vector regression chemo-metric in quantitative analysis of LIBS spectra
Authors: Taoreed Olakunle Owolabi
Mohammed Gondal
Journal: Journal of Analytical Atomic Spectrometry (2017)
Discipline: CHEMISTRY
Keywords: Laser induced breakdown spectroscopy (LIBS); elemental compositions; sample preparation; rapid analysis; real time analysis

Abstract: Laser induced breakdown spectroscopy (LIBS) is an atomic emission spectroscopy through which elemental compositions of materials can be determined with little or no sample preparation. Small sample requirement as well as it capacity for rapid and real time analysis contributes significantly to the wider applicability of the technique. However, quantitative analysis of LIBS spectra remains a challenge and requires non-linear modeling technique that fully captures the complex interactions in the laser induced plasma and ultimately reduces the effect of self-absorption. Support vector regression (SVR) recently attracts significant attention in chemo-metrics due to its sound mathematical background and unique ability to model non-linear systems with reasonable degree of precision. This work proposes two novel techniques by which the performance of SVR can be improved for the quantitative analysis of LIBS spectra. The first technique, referred to as homogeneously hybridized support vector regression (HSVR), combines two SVR algorithms in which the output of the first algorithm serves as the input to the second algorithm while the second technique, referred to as internal reference preprocessing method (IRP), uses the spectra feature that is normalized with the emission line intensity which is not significantly affected by self-absorption. The hyper-parameters of the developed models are optimized using gravitational search algorithm (GSA). On the basis of root mean square error, GSA-HSVR-WIRP (without IRP) performs better than GSA-SVR-WIRP with over 75% performance improvement while GSA-HSVR-IRP performs better than GSA-SVR-IRP with over 95% performance improvement. The outcome of this work would be very useful for precise LIBS quantitative analysis and would eventually promote wide applicability of the technique.

Retracted: Yes

PubPeer comments:
 **Retraction: Novel techniques for enhancing the performance of support vector regression chemo-metric in quantitative analysis of LIBS spectra** Jeanne Andres Royal Society of Chemistry, Thomas Graham House, Science Park, Milton Road, Cambridge, CB4 0WF, UK. E-mail: jaas-rsc@rsc.org Received 15th October 2019 , Accepted 15th October 2019 First published on 25th October 2019 Retraction of u2018Novel techniques for enhancing the performance of support vector regression chemo-metric in quantitative analysis of LIBS spectrau2019 by Taoreed Olakunle Owolabi et al., J. Anal. At. Spectrom., 2017, DOI: 10.1039 c7ja00229g. The Royal Society of Chemistry hereby wholly retracts this Journal of Analytical Atomic Spectrometry article due to concerns that the paper has crucial errors in the data. There is disagreement over the identification of spectral lines, as they do not agree with reference standards. The u2018mainu2019 lines for some elements are missing weaker than the u2018minoru2019 lines of the same element. This should not be possible, regardless of the chemometrics used and suggests that the elements have been misidentified, which calls into question the conclusions of the paper. The chemometric approach is not fully explained with regards to details of line identification and calibration. The authorsu2019 explanation for this discrepancy has been unsatisfactory. The editor is therefore retracting the paper to maintain the validity of the scientific record. The authors, Taoreed Olakunle Owolabi and Mohammed Gondal, do not agree with the retraction. Retraction endorsed by Jeanne Andres, Executive Editor, Journal of Analytical Atomic Spectrometry, 15th October 2019. See also: https: retractionwatch.com 2019 11 14 two-spectrometry-papers-retracted-one-for-intolerable-mistakes-the-authors-dont-agree ,