Intro; Preface of the Series Editor; Acknowledgements; Contents; Abbreviations; 1 Introduction; References; 2 Main Characteristics and Types of Electroanalytical Data; 2.1 Types of Data According to the Electroanalytical Processes Involved; 2.2 Types of Data According to Their Dimensions; 2.3 On the Linearity of Electrochemical Data; References; 3 Exploratory Data Analysis; 3.1 Univariate and Multivariate Data Analysis; 3.2 Data Preprocessing; 3.3 Principal Component Analysis (PCA); 3.4 Supervised Classification Methods: Linear Discriminant Analysis (LDA); References

4 Experimental Design and Optimization4.1 General Concepts: Response Surface and Factorial Design; 4.2 Experimental Design for Variable Screening and Optimization of Linear Data; 4.3 Experimental Design for Non-linear Data; 4.4 Electroanalytical Examples of Experimental Design; References; 5 Multivariate Calibration; 5.1 Classical Least Squares (CLS); 5.2 Inverse Least Squares (ILS); 5.3 Principal Component Regression (PCR); 5.4 Partial Least Squares (PLS); 5.5 Examples of Application of Linear Calibration Methods

5.6 Supervised Classification by Means of Partial Least Squares Discriminant Analysis (PLS-DA)5.7 Non-linear Methods. Artificial Neural Networks (ANN); 5.8 Multivariate Standard Addition; References; 6 Multivariate Curve Resolution; 6.1 Multivariate Curve Resolution by Alternating Least Squares (MCR-ALS): A General Overview; 6.2 Initial Estimations in MCR-ALS; 6.3 Chemical Components Versus Electrochemical Components in MCR-ALS; 6.4 Examples of Application of MCR-ALS to Electroanalytical Data; 6.5 MCR of Non-linear Data; 6.6 Three-Way Data Analysis; References; 7 Future Trends

7.1 From Knowledge-Based Expert Systems to Artificial Intelligence and Big Data7.2 Soft Modelling Versus Hard Modelling; 7.3 Electrochemical Versus Spectroscopic Measurements; 7.4 Electrochemistry and Chemometrics Versus ICP and MS; References; About the Authors; About the Series Editor; Index