Chemometric Methods in Analytical Spectroscopy Technology

✍ Scribed by Xiaoli Chu, Yue Huang, Yong-Huan Yun, Xihui Bian

• Publisher: Springer
• Year: 2022
• Tongue: English
• Leaves: 596
• Category: Library

✦ Synopsis

This book discusses chemometric methods for spectroscopy analysis including NIR, MIR, Raman, NMR, and LIBS, from the perspective of practical applied spectroscopy. It covers all aspects of chemometrics associated with analytical spectroscopy, including representative sample selection algorithm, outlier detection algorithm, model updating and maintenance algorithm and strategy and calibration performance evaluation methods.To provide a systematic and comprehensive overview the latest progress of chemometric methods including recent scientific research and practical applications are presented. In addition the book also highlights the improvement of classical algorithms and the extension of common strategies. It is therefore useful as a reference book for researchers engaged in analytical spectroscopy technology, chemometrics, analytical instruments and other related fields.

✦ Table of Contents

Preface
Contents
1 Chemometric Methods in Analytical Spectroscopy Technology
1.1 Introduction
1.1.1 Overview of Chemometrics
1.1.2 Analysis of Spectroscopy Combined with Chemometrics
1.1.3 Beginning of Modern Spectroscopy Technology—The Contribution of Karl Norris
References
2 Modern Spectral Analysis Techniques
2.1 Introduction
2.2 Near-Infrared Spectroscopy
2.2.1 Micro Near-Infrared Spectral Analysis Technology
2.2.2 Online Near-Infrared Spectral Analysis Technology
2.2.3 Standard Methods for Near-Infrared Spectroscopy
2.3 Mid-Infrared Spectroscopy
2.3.1 Portable Mid-Infrared Spectral Analysis Technology
2.3.2 Online Mid-Infrared Spectral Analysis Technology
2.4 Raman Spectroscopy
2.4.1 Fourier Transform Raman Spectroscopy
2.4.2 Surface Enhanced Raman Scattering Spectroscopy
2.4.3 Confocal Raman Spectroscopy
2.4.4 Spatial Offset Raman Spectroscopy
2.4.5 Transmitted Raman Spectroscopy
2.4.6 Portable Raman Spectral Analysis Technology
2.4.7 Fiber Raman Spectral Analysis Technology
2.5 Ultraviolet-Visible Spectroscopy
2.6 Molecular Fluorescence Spectroscopy
2.6.1 Three-Dimensional Fluorescence Spectroscopy
2.6.2 Laser-Induced Fluorescence Spectroscopy
2.7 Low-Field NMR Spectroscopy
2.8 Terahertz Spectroscopy
2.9 Laser-Induced Breakdown Spectroscopy
2.10 Spectral Imaging
References
3 Basis of Matrices and Mathematical Statistics
3.1 Basis of Matrix
3.2 Matrix Representation of Lambert-Beer’s Law
3.3 Variance and Normal Distribution
3.4 Significance Test
3.5 Correlation Coefficient
3.6 Covariance and Covariance Matrix
3.7 Multivariable Graph Representation
3.7.1 Spatial Representation of Samples
3.7.2 Box Plot
3.7.3 Radar Chart
References
4 Spectral Preprocessing Methods
4.1 Mean Centering
4.2 Auto-scaling
4.3 Normalization
4.4 Smoothing
4.4.1 Moving Average Smoothing
4.4.2 Savitzky-Golay Convolution Smoothing
4.4.3 Fourier Transform and Wavelet Transform
4.5 Continuum Removed
4.6 Adaptive Iteratively Reweighted Penalized Least Squares
4.7 Derivative
4.7.1 Norris Method
4.7.2 Savitzky-Golay Convolution for Derivative Calculation
4.7.3 Wavelet Transform for Derivative Calculation
4.7.4 Fractional Derivative
4.8 Standard Normal Variate and De-Trending
4.9 Multiplicative Scatter Correction
4.10 Vector Angle Conversion
4.11 Fourier Transform
4.12 Wavelet Transform
4.13 Image Moment Methods
4.14 External Parameter Orthogonalization
4.15 Generalized Least Squares Weighting
4.16 Loading Space Standardization
4.17 Oblique Projection
4.18 Orthogonal Signal Correction
4.18.1 Wold Algorithm
4.18.2 Fearn Algorithm
4.18.3 Direct Orthogonal Signal Correction Algorithm
4.18.4 Direct Orthogonal Algorithm
4.18.5 Application of Orthogonal Signal Correction Algorithm
4.19 Net Analyte Signal
4.20 Optical Path-Length Estimation and Correction
4.21 Two-Dimensional Correlation Spectroscopy
References
5 Wavelength Selection Methods
5.1 Correlation Coefficient and Analysis of Variance Method
5.2 Simple-To-Use Interactive Self-modeling Mixture Analysis Method
5.3 Successive Projections Algorithm
5.4 Variable Importance in Projection
5.5 Interval Partial Least Squares Method
5.6 Moving Window PLS
5.7 Recursive Weighted PLS
5.8 Elimination of Uninformative Variables
5.9 Global Optimization Methods
5.9.1 Genetic Algorithm
5.9.2 Simulated Annealing Algorithm
5.9.3 Particle Swarm Optimization
5.9.4 Ant Colony Algorithm
5.10 Model Population Analysis-Based Methods
5.10.1 Competitive Adaptive Reweighted Sampling
5.10.2 Iteratively Retaining Informative Variables
5.10.3 Variable Combination Population Analysis
5.10.4 Other Methods
5.10.5 Wavelength Selection Method Based on Hybrid Strategy
5.11 The Selection of Spectral Preprocessing and Wavelength Selection Methods
References
6 Spectral Dimensionality Reduction Methods
6.1 The Multicollinearity Problem
6.2 Principal Component Analysis
6.2.1 Theory of Principal Component Analysis
6.2.2 Determination of Principal Component Number
6.2.3 Algorithm of Principal Component Analysis
6.2.4 Application of Principal Component Analysis
6.2.5 Multivariate Resolution Alternating Least Squares
6.2.6 Band Target Entropy Minimization
6.2.7 Multilevel Simultaneous Component Analysis
6.3 Non-negative Matrix Factorization
6.4 Independent Component Analysis
6.5 Multi-dimensional Scaling Transformation
6.6 Isometric Mapping
6.7 Local Linear Embedding
6.8 T-Distributed Stochastic Neighborhood Embedding
6.9 Other Algorithms
References
7 Linear Calibration Methods
7.1 Univariate Linear Regression
7.2 Multiple Linear Regression
7.3 Concentration Residual Augmented Classical Least Squares
7.4 Stepwise Linear Regression
7.5 Ridge Regression
7.6 Lasso Regression
7.7 Least Angle Regression
7.8 Elastic Net
7.9 Principal Component Regression
7.9.1 Theory
7.9.2 Method for Selecting the Optimal PCs
7.9.3 Partial Least Squares Regression
References
8 Nonlinear Calibration Methods
8.1 Artificial Neural Network
8.1.1 Introduction
8.1.2 Back Propagation-Artificial Neural Network
8.1.3 Design of BP-ANN
8.1.4 Other Types of Neural Networks
8.1.5 Optimization of Neural Network Parameters
8.2 Support Vector Machine
8.2.1 Introduction
8.2.2 Support Vector Regression
8.2.3 Least Squares Support Vector Regression
8.2.4 Optimization of Support Vector Regression Parameters
8.3 Relevance Vector Machine
8.4 Kernel Partial Least Squares
8.5 Extreme Learning Machine
8.6 Gaussian Process Regression
References
9 Method of Selecting Calibration Samples
9.1 Introduction
9.2 Kennard-Stone Method
9.3 Sample Set Partitioning Based on Joint X–Y Distances (SPXY) Method
9.4 Optimizable K-dissimilarity Selection Method
9.5 Other Methods
References
10 Detection Methods for Outlier Samples
10.1 Detection of Outlier Samples During Calibration Process
10.2 Detection of Outlier Samples During the Prediction Process
10.3 Other Detection Methods
References
11 Maintenance and Update of Calibration Model
11.1 Necessity
11.2 Recursive Exponentially Weighted PLS
11.3 Block-Wise Recursive PLS
11.4 Just-In-Time Learning and Active Learning
References
12 Pattern Recognition Methods
12.1 Introduction
12.2 Unsupervised Pattern Recognition Methods
12.2.1 Similarity Coefficients and Distances
12.2.2 Hierarchical Cluster Analysis
12.2.3 K-Means Clustering
12.2.4 Fuzzy K-Means Clustering
12.2.5 Gaussian Mixture Model
12.2.6 Self-organizing Neural Network
12.3 Supervised Pattern Recognition Methods
12.3.1 Minimum Distance Discriminant Method
12.3.2 Canonical Variate Analysis
12.3.3 K-Nearest Neighbor
12.3.4 Soft Independent Modeling of Class Analogy
12.3.5 Logistic Regression
12.3.6 Soft-Max Classifier
12.3.7 Random Forest
12.3.8 Application of Regression Methods for Discriminant Analysis
12.4 Spectral Searching Methods
12.4.1 Introduction
12.4.2 Spectral Searching Algorithms
12.4.3 Improvements of Spectral Searching Algorithms
12.4.4 Spectral Searching Strategies and Applications
References
13 Model Evaluation
13.1 Evaluation of Quantitative Calibration Model
13.1.1 Evaluation Parameters
13.1.2 Model Evaluation
13.2 Evaluation of Performance of Pattern Recognition Model
References
14 Methods for Improving Prediction Ability of Model
14.1 Modeling Strategies for Improving the Robustness
14.2 Modeling Strategies Based on Local Samples
14.3 Ensemble Modeling Strategies
14.3.1 Bagging Ensemble Strategy
14.3.2 Boosting Ensemble Strategy
14.3.3 Stacked Ensemble Strategy
14.3.4 Stacked Generalization Strategy
14.4 Virtual Sample Modeling Strategy
14.5 Semi-supervised Learning Methods
14.6 Multi-target Regression Strategy
References
15 Multi-spectral Fusion Technology
15.1 Fusion Strategies and Methods
15.2 Multi-block Partial Least Squares Method
15.3 Sequential and Orthogonal Partial Least Squares Method
15.4 Research on Application of Multi-Spectral Fusion
15.5 Future Prospect
References
16 Multi-way Resolution and Calibration Methods
16.1 Introduction
16.2 Parallel Factor Analysis
16.3 Alternating Trilinear Decomposition
16.4 Multi-way Partial Least Squares
References
17 Calibration Transfer Methods
17.1 Introduction
17.2 Traditional Algorithms
17.2.1 Spectral Subtraction Correction
17.2.2 Shenk’s Algorithm
17.2.3 Direct Standardization
17.2.4 Piecewise Direct Standardization
17.2.5 Procrustes Analysis
17.2.6 Target Transformation Factor Analysis
17.2.7 Maximum Likelihood Principal Component Analysis
17.2.8 Slope/Bias Correction
17.3 Improvement of Traditional Algorithms
17.4 New Algorithms
17.4.1 Canonical Correlation Analysis
17.4.2 Spectral Space Transformation
17.4.3 Alternating Trilinear Decomposition
17.4.4 Multi-task Learning
17.4.5 Generalized Least Squares
17.4.6 Other Algorithms
17.5 Global Calibration, Robust Calibration, and Model Update
17.6 Progress of Applications
17.6.1 SBC Method
17.6.2 SSC Method
17.6.3 Shenk’s Method
17.6.4 DS Method
17.6.5 PDS Method
17.6.6 CCA Method
17.6.7 Establishment of Global Model
17.6.8 Other Methods
References
18 Deep Learning Methods
18.1 Stacked Auto-encoder
18.2 Convolution Neural Network
18.2.1 Basic Structure of CNN
18.2.2 Optimistic Algorithm
18.2.3 Loss Function
18.2.4 Activation Function
18.2.5 Methods to Avoid Over-Fitting
18.2.6 Classical Convolution Neural Network Architecture
18.2.7 Popular Deep Learning Software Framework
18.2.8 Design of Convolution Neural Networks
18.2.9 Training of Convolution Neural Networks
18.2.10 Advantages and Disadvantages of Convolution Neural Network
18.2.11 Applications of Convolution Neural Network
18.3 Deep Belief Network
18.4 Transfer Learning
References
19 Chemometrics Software and Toolkits
19.1 Introduction
19.2 Basic Structure and Functions of Software
19.3 Common Software and Toolkits
References
20 Discussion of Some Issues
20.1 Comparison of Different Spectroscopic Analysis
20.2 Selection of Chemometric Methods
20.2.1 Selection of Multivariate Calibration Methods
20.2.2 Selection of Pattern Recognition Methods
20.2.3 Selection of Spectral Preprocessing Methods and Spectral Variables
20.3 Influencing Factors of Model Prediction Ability
20.3.1 Effect of Calibration Samples
20.3.2 Effect of Reference Data
20.3.3 Effect of Spectral Measurement Methods
20.3.4 Effect of Spectral Acquisition Conditions
20.3.5 Effect of Instrument Performance
20.4 Outlook
References