{"product_id":"chemometrics-and-numerical-methods-in-libs-isbn-9781119759584","title":"Chemometrics and Numerical Methods in LIBS","description":"\u003cb\u003eChemometrics and Numerical Methods in LIBS\u003c\/b\u003e \u003cp\u003e\u003cb\u003eA practical guide to the application of chemometric methods to solve qualitative and quantitative problems in LIBS analyses\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eChemometrics and Numerical Methods in LIBS\u003c\/i\u003e, delivers an authoritative and practical exploration of the use of advanced chemometric methods to laser-induced breakdown spectroscopy (LIBS) cases. The book discusses the fundamentals of chemometrics before moving on to solutions that can be applied to data analysis methods. It is a concise guide designed to help readers at all levels of knowledge solve commonly encountered problems in the field. \u003c\/p\u003e\u003cp\u003eThe book includes three sections: LIBS information simplification, LIBS classification, and quantitative analysis by LIBS. Each section of the book is divided into a description of relevant techniques and practical examples of its applications. Contributors to this edited volume are the most recognized international experts on the chemometric techniques relevant to LIBS analysis. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eChemometrics and Numerical Methods in LIBS\u003c\/i\u003e also includes: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eA thorough introduction to the simplification of LIBS information, including principal component analysis, independent component analysis, and parallel factor analysis \u003c\/li\u003e \u003cli\u003eComprehensive explorations of classification by LIBS, including spectral angle mapping, linear discriminant analysis, graph clustering, self-organizing maps, and artifical neural networks \u003c\/li\u003e \u003cli\u003ePractical discussions of linear methods for quantitative analysis by LIBS, including calibration curves, partial least squares regression, and limit of detection \u003c\/li\u003e \u003cli\u003eIn-depth examinations of multivariate analysis and non-linear methods, including calibration-free LIBS, the non-linear Kalman filter, artificial and convolutional neural networks for quantification \u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eRelevant for researchers and PhD students seeking practical information on the application of advanced statistical methods to the analysis of LIBS spectra, \u003ci\u003eChemometrics and Numerical Methods in LIBS\u003c\/i\u003e will also earn a place in the libraries of students taking courses involving LIBS spectro-analytical techniques \u003c\/p\u003e\u003cp\u003eList of Contributors xiii\u003c\/p\u003e \u003cp\u003ePreface xvii\u003c\/p\u003e \u003cp\u003eIntroduction and Brief Summary of the LIBS Development 1\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Introduction to LIBS 5\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 LIBS Fundamentals 7\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMohamad Sabsabi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Interaction of Laser Beam with Matter 8\u003c\/p\u003e \u003cp\u003e1.2 Basics of Laser–Matter Interaction 9\u003c\/p\u003e \u003cp\u003e1.3 Processes in Laser-Produced Plasma 10\u003c\/p\u003e \u003cp\u003e1.4 Factors Affecting Laser Ablation and Laser-Induced Plasma Formation 11\u003c\/p\u003e \u003cp\u003e1.4.1 Influence of Laser Parameters on the Laser-Induced Plasmas 11\u003c\/p\u003e \u003cp\u003e1.4.2 Laser Wavelength (\u003ci\u003eλ\u003c\/i\u003e) 12\u003c\/p\u003e \u003cp\u003e1.4.3 Laser Pulse Duration (\u003ci\u003eτ\u003c\/i\u003e) 12\u003c\/p\u003e \u003cp\u003e1.4.4 Laser Energy (\u003ci\u003eE\u003c\/i\u003e) 13\u003c\/p\u003e \u003cp\u003e1.4.5 Influence of Ambient Gas 13\u003c\/p\u003e \u003cp\u003e1.5 Plasma Properties and Plasma Emission Spectra 14\u003c\/p\u003e \u003cp\u003eReferences 15\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 LIBS Instrumentations 19\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMohamad Sabsabi and Vincenzo Palleschi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Basics of LIBS instrumentations 19\u003c\/p\u003e \u003cp\u003e2.2 Lasers in LIBS Systems 20\u003c\/p\u003e \u003cp\u003e2.3 Desirable Requirements for Atomic Emission Spectrometers\/Detectors 22\u003c\/p\u003e \u003cp\u003e2.4 Spectrometers 23\u003c\/p\u003e \u003cp\u003e2.4.1 Czerny–Turner Optical Configuration 23\u003c\/p\u003e \u003cp\u003e2.4.2 Paschen–Runge Design 24\u003c\/p\u003e \u003cp\u003e2.4.3 Echelle Spectrometer Configuration 25\u003c\/p\u003e \u003cp\u003e2.5 Detectors 26\u003c\/p\u003e \u003cp\u003e2.5.1 Photomultiplier Detectors 26\u003c\/p\u003e \u003cp\u003e2.5.2 Solid-State Detectors 27\u003c\/p\u003e \u003cp\u003e2.5.3 The Interline CCD Detectors 27\u003c\/p\u003e \u003cp\u003e2.5.3.1 The Image Intensifier 28\u003c\/p\u003e \u003cp\u003eReferences 29\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Applications of LIBS 31\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eVincenzo Palleschi and Mohamad Sabsabi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Industrial Applications 31\u003c\/p\u003e \u003cp\u003e3.1.1 Metal Industry 31\u003c\/p\u003e \u003cp\u003e3.1.2 Energy Production 34\u003c\/p\u003e \u003cp\u003e3.2 Biomedical Applications 34\u003c\/p\u003e \u003cp\u003e3.3 Geological and Environmental Applications 36\u003c\/p\u003e \u003cp\u003e3.4 Cultural Heritage and Archaeology Applications 37\u003c\/p\u003e \u003cp\u003e3.5 Other Applications 37\u003c\/p\u003e \u003cp\u003eReferences 38\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Simplications of LIBS Information 45\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 LIBS Spectral Treatment 47\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSabrina Messaoud Aberkane, Noureddine Melikechi and Kenza Yahiaoui\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 47\u003c\/p\u003e \u003cp\u003e4.2 Baseline Correction 47\u003c\/p\u003e \u003cp\u003e4.2.1 Polynomial Algorithm 48\u003c\/p\u003e \u003cp\u003e4.2.2 Model-free Algorithm 49\u003c\/p\u003e \u003cp\u003e4.2.3 Wavelet Transform Model 52\u003c\/p\u003e \u003cp\u003e4.3 Noise Filtering 55\u003c\/p\u003e \u003cp\u003e4.3.1 Wavelet Threshold De-noising (WTD) 55\u003c\/p\u003e \u003cp\u003e4.3.2 Baseline Correction and Noise Filtering 59\u003c\/p\u003e \u003cp\u003e4.4 Overlapping Peak Resolution 60\u003c\/p\u003e \u003cp\u003e4.4.1 Curve Fitting Method 61\u003c\/p\u003e \u003cp\u003e4.4.2 The Wavelet Transform 64\u003c\/p\u003e \u003cp\u003e4.5 Features Selection 66\u003c\/p\u003e \u003cp\u003e4.5.1 Principal Component Analysis 68\u003c\/p\u003e \u003cp\u003e4.5.2 Genetic Algorithm (GA) 68\u003c\/p\u003e \u003cp\u003e4.5.3 Wavelet Transformation (WT) 68\u003c\/p\u003e \u003cp\u003eReferences 71\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Principal Component Analysis 81\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMohamed Abdel-Harith and Zienab Abdel-Salam\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 81\u003c\/p\u003e \u003cp\u003e5.1.1 Laser-Induced Breakdown Spectroscopy (LIBS) 81\u003c\/p\u003e \u003cp\u003e5.2 The Principal Component Analysis (PCA) 82\u003c\/p\u003e \u003cp\u003e5.3 PCA in Some LIBS Applications 83\u003c\/p\u003e \u003cp\u003e5.3.1 Geochemical Applications 83\u003c\/p\u003e \u003cp\u003e5.3.2 Food and Feed Applications 85\u003c\/p\u003e \u003cp\u003e5.3.3 Microbiological Applications 88\u003c\/p\u003e \u003cp\u003e5.3.4 Forensic Applications 91\u003c\/p\u003e \u003cp\u003e5.4 Conclusion 94\u003c\/p\u003e \u003cp\u003eReferences 94\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Time-Dependent Spectral Analysis 97\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eFausto Bredice, Ivan Urbina, and Vincenzo Palleschi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 97\u003c\/p\u003e \u003cp\u003e6.2 Time-Dependent LIBS Spectral Analysis 98\u003c\/p\u003e \u003cp\u003e6.2.1 Independent Component Analysis 98\u003c\/p\u003e \u003cp\u003e6.2.2 3D Boltzmann Plot 102\u003c\/p\u003e \u003cp\u003e6.2.2.1 Principles of the Method 103\u003c\/p\u003e \u003cp\u003e6.3 Applications 109\u003c\/p\u003e \u003cp\u003e6.3.1 3D Boltzmann Plot Coupled with Independent Component Analysis 109\u003c\/p\u003e \u003cp\u003e6.3.2 Analysis of a Carbon Plasma by 3D Boltzmann Plot Method 109\u003c\/p\u003e \u003cp\u003e6.3.3 Assessment of the LTE Condition Through the 3D Boltzmann Plot Method 114\u003c\/p\u003e \u003cp\u003e6.3.4 Evaluation of Self-Absorption 114\u003c\/p\u003e \u003cp\u003e6.3.5 Determination of Transition Probabilities 118\u003c\/p\u003e \u003cp\u003e6.3.6 3D Boltzmann Plot and Calibration-free Laser-induced Breakdown Spectroscopy 121\u003c\/p\u003e \u003cp\u003e6.4 Conclusion 123\u003c\/p\u003e \u003cp\u003eReferences 123\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Classification by LIBS 127\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Distance-based Method 129\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eHua Li and Tianlong Zhang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Cluster Analysis 132\u003c\/p\u003e \u003cp\u003e7.1.1 Introduction 132\u003c\/p\u003e \u003cp\u003e7.1.2 Theory 133\u003c\/p\u003e \u003cp\u003e7.1.2.1 K-means Clustering 133\u003c\/p\u003e \u003cp\u003e7.1.2.2 Hierarchical Clustering 134\u003c\/p\u003e \u003cp\u003e7.1.3 Application 135\u003c\/p\u003e \u003cp\u003e7.2 Independent Components Analysis 138\u003c\/p\u003e \u003cp\u003e7.2.1 Introduction 138\u003c\/p\u003e \u003cp\u003e7.2.2 Theory 138\u003c\/p\u003e \u003cp\u003e7.2.3 Application 140\u003c\/p\u003e \u003cp\u003e7.3 K-Nearest Neighbor 143\u003c\/p\u003e \u003cp\u003e7.3.1 Introduction 143\u003c\/p\u003e \u003cp\u003e7.3.2 Theory 143\u003c\/p\u003e \u003cp\u003e7.3.3 Application 145\u003c\/p\u003e \u003cp\u003e7.4 Linear Discriminant Analysis 145\u003c\/p\u003e \u003cp\u003e7.4.1 Introduction 145\u003c\/p\u003e \u003cp\u003e7.4.2 Theory 148\u003c\/p\u003e \u003cp\u003e7.4.2.1 The Calculation Process of LDA (Two Categories) 148\u003c\/p\u003e \u003cp\u003e7.4.3 Application 151\u003c\/p\u003e \u003cp\u003e7.5 Partial Least Squares Discriminant Analysis 153\u003c\/p\u003e \u003cp\u003e7.5.1 Introduction 153\u003c\/p\u003e \u003cp\u003e7.5.2 Theory 155\u003c\/p\u003e \u003cp\u003e7.5.3 Application 157\u003c\/p\u003e \u003cp\u003e7.6 Principal Component Analysis 161\u003c\/p\u003e \u003cp\u003e7.6.1 Introduction 161\u003c\/p\u003e \u003cp\u003e7.6.2 Theory 164\u003c\/p\u003e \u003cp\u003e7.6.3 Application 166\u003c\/p\u003e \u003cp\u003e7.7 Soft Independent Modeling of Class Analogy 174\u003c\/p\u003e \u003cp\u003e7.7.1 Introduction 174\u003c\/p\u003e \u003cp\u003e7.7.2 Theory 175\u003c\/p\u003e \u003cp\u003e7.7.3 Application 177\u003c\/p\u003e \u003cp\u003e7.8 Conclusion and Expectation 180\u003c\/p\u003e \u003cp\u003eReferences 181\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Blind Source Separation in LIBS 189\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAnna Tonazzini, Emanuele Salerno, and Stefano Pagnotta\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 189\u003c\/p\u003e \u003cp\u003e8.2 Data Model 193\u003c\/p\u003e \u003cp\u003e8.3 Analyzing LIBS Data via Blind Source Separation 193\u003c\/p\u003e \u003cp\u003e8.3.1 Second-order BSS 193\u003c\/p\u003e \u003cp\u003e8.3.2 Maximum Noise Fraction 194\u003c\/p\u003e \u003cp\u003e8.3.3 Independent Component Analysis 196\u003c\/p\u003e \u003cp\u003e8.3.4 ICA for Noisy Data 197\u003c\/p\u003e \u003cp\u003e8.4 Numerical Examples 197\u003c\/p\u003e \u003cp\u003e8.5 Final Remarks 206\u003c\/p\u003e \u003cp\u003eReferences 207\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Artificial Neural Networks for Classification 213\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJakub Vrábel, Erik Képeš, Pavel Pořízka, and Jozef Kaiser\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction and Scope 213\u003c\/p\u003e \u003cp\u003e9.2 Artificial Neural Networks (ANNs) 214\u003c\/p\u003e \u003cp\u003e9.3 Cost Functions and Training 216\u003c\/p\u003e \u003cp\u003e9.4 Backpropagation 219\u003c\/p\u003e \u003cp\u003e9.5 Convolutional Neural Networks 221\u003c\/p\u003e \u003cp\u003e9.6 Evaluation and Tuning of ANNs 224\u003c\/p\u003e \u003cp\u003e9.7 Regularization 227\u003c\/p\u003e \u003cp\u003e9.8 State-of-the-art LIBS Classification Using ANNs 229\u003c\/p\u003e \u003cp\u003e9.9 Summary 233\u003c\/p\u003e \u003cp\u003eAcknowledgments 234\u003c\/p\u003e \u003cp\u003eReferences 234\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Data Fusion: LIBS + Raman 241\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eBeatrice Campanella and Stefano Legnaioli\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 241\u003c\/p\u003e \u003cp\u003e10.2 Data Fusion Background 242\u003c\/p\u003e \u003cp\u003e10.3 Data Treatment 244\u003c\/p\u003e \u003cp\u003e10.4 Working with Images 245\u003c\/p\u003e \u003cp\u003e10.4.1 Vectors Concatenation 246\u003c\/p\u003e \u003cp\u003e10.4.2 Vectors Co-addition 246\u003c\/p\u003e \u003cp\u003e10.4.3 Vectors Outer Sum 246\u003c\/p\u003e \u003cp\u003e10.4.4 Vectors Outer Product 247\u003c\/p\u003e \u003cp\u003e10.4.5 Data Analysis 247\u003c\/p\u003e \u003cp\u003e10.5 Applications 248\u003c\/p\u003e \u003cp\u003e10.6 Conclusion 253\u003c\/p\u003e \u003cp\u003eReferences 253\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV Quantitative Analysis 257\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Univariate Linear Methods 259\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eStefano Legnaioli, Asia Botto, Beatrice Campanella, Francesco Poggialini, Simona Raneri, and Vincenzo Palleschi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Standards 259\u003c\/p\u003e \u003cp\u003e11.2 Matrix Effect 260\u003c\/p\u003e \u003cp\u003e11.3 Normalization 261\u003c\/p\u003e \u003cp\u003e11.4 Linear vs. Nonlinear Calibration Curves 264\u003c\/p\u003e \u003cp\u003e11.5 Figures of Merit of a Calibration Curve 267\u003c\/p\u003e \u003cp\u003e11.5.1 Coefficient of Determination 270\u003c\/p\u003e \u003cp\u003e11.5.2 Root Mean Squared Error of Calibration 270\u003c\/p\u003e \u003cp\u003e11.5.3 Limit of Detection 270\u003c\/p\u003e \u003cp\u003e11.6 Inverse Calibration 273\u003c\/p\u003e \u003cp\u003e11.7 Conclusion 274\u003c\/p\u003e \u003cp\u003eReferences 274\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Partial Least Squares 277\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eZongyu Hou, Weiran Song, and Zhe Wang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Overview 277\u003c\/p\u003e \u003cp\u003e12.2 Partial Least Squares Regression Algorithms 278\u003c\/p\u003e \u003cp\u003e12.2.1 Nonlinear Iterative PLS 278\u003c\/p\u003e \u003cp\u003e12.2.2 SIMPLS Algorithm 279\u003c\/p\u003e \u003cp\u003e12.2.3 Kernel Partial Least Squares 279\u003c\/p\u003e \u003cp\u003e12.2.4 Locally Weighted Partial Least Squares 280\u003c\/p\u003e \u003cp\u003e12.2.5 Dominant Factor-based Partial Least Squares 281\u003c\/p\u003e \u003cp\u003e12.3 Partial Least Squares Discriminant Analysis 282\u003c\/p\u003e \u003cp\u003e12.4 Results of Partial Least Squares in LIBS 283\u003c\/p\u003e \u003cp\u003e12.4.1 Coal Analysis 283\u003c\/p\u003e \u003cp\u003e12.4.2 Metal Analysis 285\u003c\/p\u003e \u003cp\u003e12.4.3 Rocks, Soils, and Minerals Analysis 285\u003c\/p\u003e \u003cp\u003e12.4.4 Organics Analysis 291\u003c\/p\u003e \u003cp\u003e12.5 Conclusion 291\u003c\/p\u003e \u003cp\u003eReferences 295\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Nonlinear Methods 303\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eFrancesco Poggialini, Asia Botto, Beatrice Campanella, Stefano Legnaioli, Simona Raneri, and Vincenzo Palleschi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 303\u003c\/p\u003e \u003cp\u003e13.2 Multivariate Nonlinear Algorithms 304\u003c\/p\u003e \u003cp\u003e13.2.1 Artificial Neural Networks 304\u003c\/p\u003e \u003cp\u003e13.2.1.1 Conventional Artificial Neural Networks 304\u003c\/p\u003e \u003cp\u003e13.2.1.2 Convolutional Neural Networks 310\u003c\/p\u003e \u003cp\u003e13.2.2 Other Nonlinear Multivariate Approaches 312\u003c\/p\u003e \u003cp\u003e13.2.2.1 The Franzini–Leoni Method 312\u003c\/p\u003e \u003cp\u003e13.2.2.2 The Kalman Filter Approach 313\u003c\/p\u003e \u003cp\u003e13.2.2.3 Calibration-Free Methods 314\u003c\/p\u003e \u003cp\u003e13.3 Conclusion 315\u003c\/p\u003e \u003cp\u003eReferences 316\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Laser Ablation-based Techniques – Data Fusion 321\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJhanis Gonzalez\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 321\u003c\/p\u003e \u003cp\u003e14.2 Data Fusion of Multiple Analytical Techniques 322\u003c\/p\u003e \u003cp\u003e14.2.1 Low-level Fusion 322\u003c\/p\u003e \u003cp\u003e14.2.2 Mid-level Fusion 323\u003c\/p\u003e \u003cp\u003e14.2.3 High-level Fusion 324\u003c\/p\u003e \u003cp\u003e14.3 Data Fusion of Laser Ablation-Based Techniques 324\u003c\/p\u003e \u003cp\u003e14.3.1 Introduction 324\u003c\/p\u003e \u003cp\u003e14.3.2 Classification of Edible Salts 326\u003c\/p\u003e \u003cp\u003e14.3.2.1 LIBS and LA-ICP-MS Measurements of the Salt Samples 327\u003c\/p\u003e \u003cp\u003e14.3.2.2 Mid-Level Data Fusion of LIBS and LA-ICP-MS of Salt Samples 327\u003c\/p\u003e \u003cp\u003e14.3.2.3 PLS-DA Classification Model for Salt Samples 333\u003c\/p\u003e \u003cp\u003e14.3.3 Coal Discrimination Analysis 334\u003c\/p\u003e \u003cp\u003e14.3.3.1 LIBS and LA-ICP-TOF-MS Measurements of the Coal Samples 335\u003c\/p\u003e \u003cp\u003e14.3.3.2 Mid-Level Data Fusion of LIBS and LA-ICP-TOF-MS of Coal Samples 335\u003c\/p\u003e \u003cp\u003e14.3.3.3 PCA Combined with K-means Cluster Analysis for Coal Samples 338\u003c\/p\u003e \u003cp\u003e14.3.3.4 PLS-DA and SVM for Coal Samples Analysis 340\u003c\/p\u003e \u003cp\u003e14.4 Comments and Future Developments 341\u003c\/p\u003e \u003cp\u003eAcknowledgments 343\u003c\/p\u003e \u003cp\u003eReferences 343\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart V Conclusions 347\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Conclusion 349\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eVincenzo Palleschi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIndex 351\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eVincenzo Palleschi\u003c\/b\u003e is a Senior Researcher with the Institute of Chemistry of Organometallic Compounds, Italian Research Council and Professor of Advanced Analytical Chemistry at the University of Pisa. He is Associate Editor of the \u003ci\u003eJournal of Advanced Research\u003c\/i\u003e and a member of the Editorial Advisory Boards of \u003ci\u003eSpectrochimica Acta B\u003c\/i\u003e and \u003ci\u003eReviews in Analytical Chemistry.\u003c\/i\u003e He has published more than 140 scientific papers on LIBS, making him the most productive author in LIBS ever. His paper on Calibration-Free LIBS, published in 1999, is the most quoted research paper in LIBS. In 2000 he was the organizer and chairperson of the First International Conference on LIBS in Pisa (Italy).   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eA practical guide to the application of chemometric methods to solve qualitative and quantitative problems in LIBS analyses\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eChemometrics and Numerical Methods in LIBS\u003c\/i\u003e, delivers an authoritative and practical exploration of the use of advanced chemometric methods to laser-induced breakdown spectroscopy (LIBS) cases. The book discusses the fundamentals of chemometrics before moving on to solutions that can be applied to data analysis methods. It is a concise guide designed to help readers at all levels of knowledge solve commonly encountered problems in the field. \u003c\/p\u003e\u003cp\u003eThe book includes three sections: LIBS information simplification, LIBS classification, and quantitative analysis by LIBS. Each section of the book is divided into a description of relevant techniques and practical examples of its applications. Contributors to this edited volume are the most recognized international experts on the chemometric techniques relevant to LIBS analysis. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eChemometrics and Numerical Methods in LIBS\u003c\/i\u003e also includes: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eA thorough introduction to the simplification of LIBS information, including principal component analysis, independent component analysis, and parallel factor analysis \u003c\/li\u003e \u003cli\u003eComprehensive explorations of classification by LIBS, including spectral angle mapping, linear discriminant analysis, graph clustering, self-organizing maps, and artifical neural networks \u003c\/li\u003e \u003cli\u003ePractical discussions of linear methods for quantitative analysis by LIBS, including calibration curves, partial least squares regression, and limit of detection \u003c\/li\u003e \u003cli\u003eIn-depth examinations of multivariate analysis and non-linear methods, including calibration-free LIBS, the non-linear Kalman filter, artificial and convolutional neural networks for quantification \u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eRelevant for researchers and PhD students seeking practical information on the application of advanced statistical methods to the analysis of LIBS spectra, \u003ci\u003eChemometrics and Numerical Methods in LIBS\u003c\/i\u003e will also earn a place in the libraries of students taking courses involving LIBS spectro-analytical techniques.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988910424293,"sku":"NP9781119759584","price":165.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119759584.jpg?v=1761782017","url":"https:\/\/k12savings.com\/es\/products\/chemometrics-and-numerical-methods-in-libs-isbn-9781119759584","provider":"K12savings","version":"1.0","type":"link"}