Impact of Laser-Induced Breakdown Spectroscopy data normalization on multivariate classification accuracy

Multivariate data analysis (MVDA) is getting popular across the spectroscopic community. To assess accurate results, the obtained data should be preprocessed prior to utilization of any MVDA algorithm. The process of data normalization or “internal standardization” is widely used across a broad range of applications. In this manuscript we investigate the utilization of Laser-Induced Breakdown Spectroscopy (LIBS) coupled with MVDA. However, many articles regarding the use of MVDA on data from LIBS do not provide any information about the data pretreatment. This work describes the impact of LIBS data normalization approaches on MVDA classification accuracy. Also, the impact of classical data preprocessing (mean centering and scaling) exploiting the prior utilization of MVDA was studied. This issue was investigated exploiting simple soft independent modelling of class analogies algorithm. The findings were generalized for three sample matrices (steel, Al alloys, and sedimentary ores). Furthermore, the selection of an appropriate normalization algorithm is not trivial since the spectrum of each sample matrix is composed of a different number of elements and corresponding elemental lines.

[1]  F. J. Fortes,et al.  The development of fieldable laser-induced breakdown spectrometer: No limits on the horizon , 2010 .

[2]  Israel Schechter,et al.  Laser-Induced Breakdown Spectroscopy (LIBS): Preface , 2006 .

[3]  Timur A. Labutin,et al.  A review of normalization techniques in analytical atomic spectrometry with laser sampling: From single to multivariate correction , 2010 .

[4]  J. D. Winefordner,et al.  Identification of Solid Materials by Correlation Analysis Using a Microscopic Laser-Induced Plasma Spectrometer , 1999 .

[5]  Xu Wang,et al.  Advanced statistical analysis of laser-induced breakdown spectroscopy data to discriminate sedimentary rocks based on Czerny–Turner and Echelle spectrometers , 2014 .

[6]  M. Sabsabi,et al.  Laser-induced breakdown spectroscopy with artificial neural network processing for material identification , 2010 .

[7]  O. Musset,et al.  A review of the development of portable laser induced breakdown spectroscopy and its applications , 2014 .

[8]  Timothy G. Rials,et al.  Analysis of preservative-treated wood by multivariate analysis of laser-induced breakdown spectroscopy spectra , 2005 .

[9]  Frank C. De Lucia,et al.  Influence of variable selection on partial least squares discriminant analysis models for explosive residue classification , 2011 .

[10]  Roger C. Wiens,et al.  Independent component analysis classification of laser induced breakdown spectroscopy spectra , 2013 .

[11]  N. Omenetto,et al.  Laser-Induced Breakdown Spectroscopy (LIBS), Part I: Review of Basic Diagnostics and Plasma—Particle Interactions: Still-Challenging Issues within the Analytical Plasma Community , 2010, Applied spectroscopy.

[12]  Pavel Pořízka,et al.  Laser-Induced Breakdown Spectroscopy coupled with chemometrics for the analysis of steel: The issue of spectral outliers filtering , 2016 .

[13]  D. Cremers,et al.  The Use of Laser-Induced Breakdown Spectroscopy for Distinguishing between Bacterial Pathogen Species and Strains , 2010, Applied spectroscopy.

[14]  R. Noll,et al.  Laser-induced breakdown spectroscopy expands into industrial applications , 2014 .

[15]  Arnab Sarkar,et al.  Advancing the analytical capabilities of laser ablation molecular isotopic spectrometry for boron isotopic analysis , 2014 .

[16]  Bret C. Windom,et al.  Laser ablation—laser induced breakdown spectroscopy (LA-LIBS): A means for overcoming matrix effects leading to improved analyte response , 2009 .

[17]  René Kizek,et al.  Trace elemental analysis by laser-induced breakdown spectroscopy—Biological applications , 2012 .

[18]  Lidiane Cristina Nunes,et al.  Laser-induced breakdown spectroscopy for analysis of plant materials: A review , 2012 .

[19]  Shane C. Burgess,et al.  Preliminary evaluation of laser-induced breakdown spectroscopy for tissue classification , 2009 .

[20]  Kevin L. McNesby,et al.  Investigation of statistics strategies for improving the discriminating power of laser-induced breakdown spectroscopy for chemical and biological warfare agent simulants , 2005 .

[21]  Richard R. Hark,et al.  Applications of laser-induced breakdown spectroscopy for geochemical and environmental analysis: A comprehensive review , 2013 .

[22]  S. Buckley,et al.  Laser-Induced Breakdown Spectroscopy Detection and Classification of Biological Aerosols , 2003, Applied spectroscopy.

[23]  K. Novotný,et al.  A versatile interaction chamber for laser-based spectroscopic applications, with the emphasis on Laser-Induced Breakdown Spectroscopy , 2014 .

[24]  S. Maurice,et al.  Feasibility study of rock identification at the surface of Mars by remote laser-induced breakdown spectroscopy and three chemometric methods , 2007 .

[25]  Pavel Zemánek,et al.  Algal Biomass Analysis by Laser-Based Analytical Techniques—A Review , 2014, Sensors.

[26]  Chase A. Munson,et al.  Laser-induced breakdown spectroscopy for detection of explosives residues: a review of recent advances, challenges, and future prospects , 2009, Analytical and bioanalytical chemistry.

[27]  J. O. Cáceres,et al.  Evaluation of supervised chemometric methods for sample classification by Laser Induced Breakdown Spectroscopy , 2015 .

[28]  Israel Schechter,et al.  Laser-induced breakdown spectroscopy (LIBS) : fundamentals and applications , 2006 .

[29]  Ulrich Panne,et al.  Multivariate classification of pigments and inks using combined Raman spectroscopy and LIBS , 2012, Analytical and Bioanalytical Chemistry.

[30]  Karel Novotný,et al.  Correlation of acoustic and optical emission signals produced at 1064 and 532 nm laser-induced breakdown spectroscopy (LIBS) of glazed wall tiles , 2009 .

[31]  R. C. Wiens,et al.  Nonlinear mapping technique for data visualization and clustering assessment of LIBS data: application to ChemCam data , 2011, Analytical and bioanalytical chemistry.

[32]  Richard R. Hark,et al.  Geographical analysis of “conflict minerals” utilizing laser-induced breakdown spectroscopy , 2012 .

[33]  Stewart Clegg,et al.  Clustering and training set selection methods for improving the accuracy of quantitative laser induced breakdown spectroscopy , 2012 .

[34]  Nicoló Omenetto,et al.  Laser-Induced Breakdown Spectroscopy (LIBS), Part II: Review of Instrumental and Methodological Approaches to Material Analysis and Applications to Different Fields , 2012, Applied spectroscopy.

[35]  Richard G. Brereton,et al.  Applied Chemometrics for Scientists , 2007 .

[36]  Lionel Canioni,et al.  Good practices in LIBS analysis: Review and advices , 2014 .

[37]  Anders Larsson,et al.  Impact of data reduction on multivariate classification models built on spectral data from bio-samples , 2015 .

[38]  Marek Semela,et al.  Detection of tire tread particles using laser-induced breakdown spectroscopy☆ , 2015 .

[39]  S J Rehse,et al.  Laser-induced breakdown spectroscopy (LIBS): an overview of recent progress and future potential for biomedical applications , 2012, Journal of medical engineering & technology.

[40]  E. Pereira-Filho,et al.  Twelve different types of data normalization for the proposition of classification, univariate and multivariate regression models for the direct analyses of alloys by laser-induced breakdown spectroscopy (LIBS) , 2016 .

[41]  Jan Hannig,et al.  Support vector machine classification of suspect powders using laser‐induced breakdown spectroscopy (LIBS) spectral data , 2012 .

[42]  Reinhard Noll,et al.  Laser-Induced Breakdown Spectroscopy: Fundamentals and Applications , 2012 .

[43]  Russell S. Harmon,et al.  Multivariate analysis of laser-induced breakdown spectroscopy chemical signatures for geomaterial classification , 2009 .