LEADER 10055nam 2200505 450 001 9910831166903321 005 20230302205144.0 010 $a1-119-75961-7 010 $a1-119-75957-9 010 $a1-119-75956-0 035 $a(MiAaPQ)EBC7107639 035 $a(Au-PeEL)EBL7107639 035 $a(CKB)24995993100041 035 $a(EXLCZ)9924995993100041 100 $a20230302d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aChemometrics and numerical methods in LIBS /$fedited by Vincenzo Palleschi 210 1$aHoboken, New Jersey :$cJohn Wiley & Sons, Incorporated,$d[2022] 210 4$dİ2022 215 $a1 online resource (381 pages) 311 08$aPrint version: Palleschi, Vincenzo Chemometrics and Numerical Methods in LIBS Newark : John Wiley & Sons, Incorporated,c2022 9781119759584 320 $aIncludes bibliographical references and index. 327 $aCover -- Title Page -- Copyright Page -- Contents -- List of Contributors -- Preface -- Introduction and Brief Summary of the LIBS Development -- Part I Introduction to LIBS -- Chapter 1 LIBS Fundamentals -- 1.1 Interaction of Laser Beam with Matter -- 1.2 Basics of Laser-Matter Interaction -- 1.3 Processes in Laser-Produced Plasma -- 1.4 Factors Affecting Laser Ablation and Laser-Induced Plasma Formation -- 1.4.1 Influence of Laser Parameters on the Laser-Induced Plasmas -- 1.4.2 Laser Wavelength (?) -- 1.4.3 Laser Pulse Duration (?) -- 1.4.4 Laser Energy (E) -- 1.4.5 Influence of Ambient Gas -- 1.5 Plasma Properties and Plasma Emission Spectra -- References -- Chapter 2 LIBS Instrumentations -- 2.1 Basics of LIBS instrumentations -- 2.2 Lasers in LIBS Systems -- 2.3 Desirable Requirements for Atomic Emission Spectrometers/Detectors -- 2.4 Spectrometers -- 2.4.1 Czerny-Turner Optical Configuration -- 2.4.2 Paschen-Runge Design -- 2.4.3 Echelle Spectrometer Configuration -- 2.5 Detectors -- 2.5.1 Photomultiplier Detectors -- 2.5.2 Solid-State Detectors -- 2.5.3 The Interline CCD Detectors -- 2.5.3.1 The Image Intensifier -- References -- Chapter 3 Applications of LIBS -- 3.1 Industrial Applications -- 3.1.1 Metal Industry -- 3.1.2 Energy Production -- 3.2 Biomedical Applications -- 3.3 Geological and Environmental Applications -- 3.4 Cultural Heritage and Archaeology Applications -- 3.5 Other Applications -- References -- Part II Simplications of LIBS Information -- Chapter 4 LIBS Spectral Treatment -- 4.1 Introduction -- 4.2 Baseline Correction -- 4.2.1 Polynomial Algorithm -- 4.2.2 Model-free Algorithm -- 4.2.3 Wavelet Transform Model -- 4.3 Noise Filtering -- 4.3.1 Wavelet Threshold De-noising (WTD) -- 4.3.2 Baseline Correction and Noise Filtering -- 4.4 Overlapping Peak Resolution. 327 $a4.4.1 Curve Fitting Method -- 4.4.2 The Wavelet Transform -- 4.5 Features Selection -- 4.5.1 Principal Component Analysis -- 4.5.2 Genetic Algorithm (GA) -- 4.5.3 Wavelet Transformation (WT) -- References -- Chapter 5 Principal Component Analysis -- 5.1 Introduction -- 5.1.1 Laser-Induced Breakdown Spectroscopy (LIBS) -- 5.2 The Principal Component Analysis (PCA) -- 5.3 PCA in Some LIBS Applications -- 5.3.1 Geochemical Applications -- 5.3.2 Food and Feed Applications -- 5.3.3 Microbiological Applications -- 5.3.4 Forensic Applications -- 5.4 Conclusion -- References -- Chapter 6 Time-Dependent Spectral Analysis -- 6.1 Introduction -- 6.2 Time-Dependent LIBS Spectral Analysis -- 6.2.1 Independent Component Analysis -- 6.2.2 3D Boltzmann Plot -- 6.2.2.1 Principles of the Method -- 6.3 Applications -- 6.3.1 3D Boltzmann Plot Coupled with Independent Component Analysis -- 6.3.2 Analysis of a Carbon Plasma by 3D Boltzmann Plot Method -- 6.3.3 Assessment of the LTE Condition Through the 3D Boltzmann Plot Method -- 6.3.4 Evaluation of Self-Absorption -- 6.3.5 Determination of Transition Probabilities -- 6.3.6 3D Boltzmann Plot and Calibration-free Laser-induced Breakdown Spectroscopy -- 6.4 Conclusion -- References -- Part III Classification by LIBS -- Chapter 7 Distance-based Method -- 7.1 Cluster Analysis -- 7.1.1 Introduction -- 7.1.2 Theory -- 7.1.2.1 K-means Clustering -- 7.1.2.2 Hierarchical Clustering -- 7.1.3 Application -- 7.2 Independent Components Analysis -- 7.2.1 Introduction -- 7.2.2 Theory -- 7.2.3 Application -- 7.3 K-Nearest Neighbor -- 7.3.1 Introduction -- 7.3.2 Theory -- 7.3.3 Application -- 7.4 Linear Discriminant Analysis -- 7.4.1 Introduction -- 7.4.2 Theory -- 7.4.2.1 The Calculation Process of LDA .(Two Categories) -- 7.4.3 Application. 327 $a7.5 Partial Least Squares Discriminant Analysis -- 7.5.1 Introduction -- 7.5.2 Theory -- 7.5.3 Application -- 7.6 Principal Component Analysis -- 7.6.1 Introduction -- 7.6.2 Theory -- 7.6.3 Application -- 7.7 Soft Independent Modeling of Class Analogy -- 7.7.1 Introduction -- 7.7.2 Theory -- 7.7.3 Application -- 7.8 Conclusion and Expectation -- References -- Chapter 8 Blind Source Separation in LIBS -- 8.1 Introduction -- 8.2 Data Model -- 8.3 Analyzing LIBS Data via Blind Source Separation -- 8.3.1 Second-order BSS -- 8.3.2 Maximum Noise Fraction -- 8.3.3 Independent Component Analysis -- 8.3.4 ICA for Noisy Data -- 8.4 Numerical Examples -- 8.5 Final Remarks -- References -- Chapter 9 Artificial Neural Networks for Classification -- 9.1 Introduction and Scope -- 9.2 Artificial Neural Networks (ANNs) -- 9.3 Cost Functions and Training -- 9.4 Backpropagation -- 9.5 Convolutional Neural Networks -- 9.6 Evaluation and Tuning of ANNs -- 9.7 Regularization -- 9.8 State-of-the-art LIBS Classification Using ANNs -- 9.9 Summary -- Acknowledgments -- References -- Chapter 10 Data Fusion: LIBS + Raman -- 10.1 Introduction -- 10.2 Data Fusion Background -- 10.3 Data Treatment -- 10.4 Working with Images -- 10.4.1 Vectors Concatenation -- 10.4.2 Vectors Co-addition -- 10.4.3 Vectors Outer Sum -- 10.4.4 Vectors Outer Product -- 10.4.5 Data Analysis -- 10.5 Applications -- 10.6 Conclusion -- References -- Part IV Quantitative Analysis -- Chapter 11 Univariate Linear Methods -- 11.1 Standards -- 11.2 Matrix Effect -- 11.3 Normalization -- 11.4 Linear vs Nonlinear Calibration Curves -- 11.5 Figures of Merit of a Calibration Curve -- 11.5.1 Coefficient of Determination -- 11.5.2 Root Mean Squared Error of Calibration -- 11.5.3 Limit of Detection -- 11.6 Inverse Calibration -- 11.7 Conclusion -- References. 327 $aChapter 12 Partial Least Squares -- 12.1 Overview -- 12.2 Partial Least Squares Regression Algorithms -- 12.2.1 Nonlinear Iterative PLS -- 12.2.2 SIMPLS Algorithm -- 12.2.3 Kernel Partial Least Squares -- 12.2.4 Locally Weighted Partial Least Squares -- 12.2.5 Dominant Factor-based Partial Least Squares -- 12.3 Partial Least Squares Discriminant Analysis -- 12.4 Results of Partial Least Squares in LIBS -- 12.4.1 Coal Analysis -- 12.4.2 Metal Analysis -- 12.4.3 Rocks, Soils, and Minerals Analysis -- 12.4.4 Organics Analysis -- 12.5 Conclusion -- References -- Chapter 13 Nonlinear Methods -- 13.1 Introduction -- 13.2 Multivariate Nonlinear Algorithms -- 13.2.1 Artificial Neural Networks -- 13.2.1.1 Conventional Artificial Neural Networks -- 13.2.1.2 Convolutional Neural Networks -- 13.2.2 Other Nonlinear Multivariate Approaches -- 13.2.2.1 The Franzini-Leoni Method -- 13.2.2.2 The Kalman Filter Approach -- 13.2.2.3 Calibration-Free Methods -- 13.3 Conclusion -- References -- Chapter 14 Laser Ablation-based Techniques - Data Fusion -- 14.1 Introduction -- 14.2 Data Fusion of Multiple Analytical Techniques -- 14.2.1 Low-level Fusion -- 14.2.2 Mid-level Fusion -- 14.2.3 High-level Fusion -- 14.3 Data Fusion of Laser Ablation-Based Techniques -- 14.3.1 Introduction -- 14.3.2 Classification of Edible Salts -- 14.3.2.1 LIBS and LA-ICP-MS Measurements of the Salt Samples -- 14.3.2.2 Mid-Level Data Fusion of LIBS and LA-ICP-MS of Salt Samples -- 14.3.2.3 PLS-DA Classification Model for Salt Samples -- 14.3.3 Coal Discrimination Analysis -- 14.3.3.1 LIBS and LA-ICP-TOF-MS Measurements of the Coal Samples -- 14.3.3.2 Mid-Level Data Fusion of LIBS and LA-ICP-TOF-MS of Coal Samples -- 14.3.3.3 PCA Combined with K-means Cluster Analysis for Coal Samples -- 14.3.3.4 PLS-DA and SVM for Coal Samples Analysis. 327 $a14.4 Comments and Future Developments -- Acknowledgments -- References -- Part V Conclusions -- Chapter 15 Conclusion -- Index -- EULA. 330 $a"The LIBS community is constantly growing and it now widely recognized that LIBS techniques provide large sets of data statistical methods for simplification and analysis are essential; however, there is still some confusion on the proper application of these methods to LIBS analysis and, most of all, on the procedures that must be applied for the validation of the results obtained. The book will be organized in three parts, dealing with the main applications of chemometrics in LIBS: simplification of the spectral information, classification of spectra, quantitative analysis. Each part will be divided in sections, describing the different techniques with practical examples. For example, in Part 2, the techniques described will be discussed in the perspective of waste sorting by LIBS and LIBS micro-imaging of geological material, to mention just two of the main applications which exploits at the best the chemometric techniques of classification."--$cProvided by publisher. 606 $aChemometrics 606 $aLaser-induced breakdown spectroscopy 615 0$aChemometrics. 615 0$aLaser-induced breakdown spectroscopy. 676 $a543.015195 702 $aPalleschi$b V. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910831166903321 996 $aChemometrics and numerical methods in LIBS$93981748 997 $aUNINA