{"product_id":"chemometrics-and-numerical-methods-in-libs-9781119759584","title":"Chemometrics and Numerical Methods in LIBS","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\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","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default 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