PRINCIPAL COMPONENT ANALYSIS (PCA)
1. Introduction (1/2)
2. Introduction (2/2)
3. Theory (1/2)
4. Theory (2/2)
5. Number of components – cross validation
6. Outliers (1/2)
7. Outliers (2/2)
8. Appendix 1: Introductory video
9. Appendix 2: Auto-scaling
10. Appendix 3: Residuals and leverage

PARTIAL LEAST SQUARES-REGRESSION 
1. Partial Least Squares Regression 1 Introduction (1/4)
2. Partial Least Squares Regression 1 Introduction (2/4)
3. Partial Least Squares Regression 1 Introduction5 (3/4)
4. Partial Least Squares Regression 1 Introduction (4/4)
5. Partial Least Squares Regression 2 Validation (1/2)
6. Partial Least Squares Regression 2 Validation (2/2)
7. Partial Least Squares Regression 3 Pre-processing (1/2)
8. Partial Least Squares Regression 4 Variable selection
9. Partial Least Squares Regression 5 Additional information

SPECTRAL PRE-PROCESSING
1. NIR pre-processing (1/2)
2. NIR pre-processing (2/2)

MULTI-WAY FLUORESCENCE
Coming soon

PARAFAC
Coming soon

MISCELLANEOUS
1. Spectral projection. Coming soon
 

Introduction 1

Introduction 2

Theory 1

 Theory 2

Number of components – cross validation

Outliers 1

Outliers 2

Appendix 1: Introductory video

Appendix 2: Auto-scaling

Appendix 3: Residuals and leverage

Partial Least Squares Regression 1 Introduction (1/4)

Partial Least Squares Regression 1 Introduction (2/4)

Least Squares Regression 1 Introduction5 (3/4)

Partial Least Squares Regression 1 Introduction (4/4)

Partial Least Squares Regression 2 Validation (1/2)

Partial Least Squares Regression 2 Validation (2/2)

Partial Least Squares Regression 3 Pre-processing (1/2)

Partial Least Squares Regression 4 Variable selection

Partial Least Squares Regression 5 Additional information

NIR pre-processing (1/2)

NIR pre-processing (2/2)