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YOUTUBE LESSONS IN CHEMOMETRICS

 

CLICK HERE TO VISIT OUR CHANNEL (Quality And Technology)

 

We have re-organize the videos in the channel. Now all videos are included in different playlists. This way, it is easier to find them. The playlists are thought to be small courses of a specific topic. 

Check the links below to go directly to the playlist in YOUTUBE:

 

     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

 

     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

 

     1. NIR pre-processing (1/2)
     2. NIR pre-processing (2/2)

 

     1. Variable selection (1/2)
     2. Variable selection (2/2)

 

 

     1. What is multi-way data (just a short intro to where we see multi-way data)
     1b. What is multi-way data. MATLAB version (an intro to the EEM data and dataset object)
     2. PARAFAC

          2.1a. The PARAFAC model (the basic PARAFAC model)

          2.1b. The PARAFAC model. MATLAB version (fitting PARAFAC in MATLAB)

          2.2a. What is good about PARAFAC (uniqueness, noise reduction, missing data)
          2.2b. What is good about PARAFAC. MATLAB version (unique models in MATLAB)
          2.3a. The algorithm (about alternating least squares)
          2.3b. The algorithm. MATLAB version (how to assess and handle convergence problems)
          2.4a. Number of components and outliers (core consistency and split-half)
          2.4b. Number of components and outliers. MATLAB version (visualizing PARAFAC models)
          2.5a. Applications (fluorescence EEM applications)
          2.5b. More applications (low- and high-field NMR - DOSY)
          2.6a. Constraints (nonnegativity and beyond)
          2.6b. Constraints. MATLAB version (and how to do it in MATLAB)
     3. Concluding

 

     1. Basic definition of Mutivariate Data Analysis - Chemometrics

 

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