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Record Nr. |
UNINA9910623989403321 |
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Autore |
Palleschi Vincenzo |
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Titolo |
Chemometrics and Numerical Methods in LIBS |
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Pubbl/distr/stampa |
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Newark : , : John Wiley & Sons, Incorporated, , 2022 |
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©2022 |
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ISBN |
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1-119-75961-7 |
1-119-75957-9 |
1-119-75956-0 |
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Descrizione fisica |
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1 online resource (381 pages) |
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Disciplina |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Cover -- 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 (λ) -- 1.4.3 Laser Pulse Duration (τ) -- 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 |
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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. |
4.4.1 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. |
7.5 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 |
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-- 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. |
Chapter 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. |
14.4 Comments and Future Developments -- Acknowledgments -- References -- Part V Conclusions -- Chapter 15 Conclusion -- Index -- EULA. |
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