| Description |
1 online resource (381 pages) |
| Contents |
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 |
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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 -- 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 |
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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 |
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14.4 Comments and Future Developments -- Acknowledgments -- References -- Part V Conclusions -- Chapter 15 Conclusion -- Index -- EULA |
| Summary |
Chemometrics and Numerical Methods in LIBS A practical guide to the application of chemometric methods to solve qualitative and quantitative problems in LIBS analyses Chemometrics and Numerical Methods in LIBS, 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. The 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. Chemometrics and Numerical Methods in LIBS also includes: A thorough introduction to the simplification of LIBS information, including principal component analysis, independent component analysis, and parallel factor analysis Comprehensive explorations of classification by LIBS, including spectral angle mapping, linear discriminant analysis, graph clustering, self-organizing maps, and artifical neural networks Practical discussions of linear methods for quantitative analysis by LIBS, including calibration curves, partial least squares regression, and limit of detection In-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 Relevant for researchers and PhD students seeking practical information on the application of advanced statistical methods to the analysis of LIBS spectra, Chemometrics and Numerical Methods in LIBS will also earn a place in the libraries of students taking courses involving LIBS spectro-analytical techniques |
| Notes |
Description based on publisher supplied metadata and other sources |
| Subject |
Laser-induced breakdown spectroscopy.
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Chemometrics.
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Analytic.
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Chemistry.
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SCIENCE.
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| Form |
Electronic book
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| ISBN |
1119759617 |
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9781119759614 |
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1119759579 |
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9781119759577 |
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1119759560 |
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9781119759560 |
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