See how to:

- Build a sustainable empirical model based on spectral data/wavelengths
- Examine data using Graph Builder to get idea of what different spectra look like

- Use Multivariate Analysis to examine all wave lengths and resulting Correlation Coefficients to confirm multicolinearity
- Use Model-Driven Multivariate Control Charts to examine all wave lengths variables, drill into runs that are out of control and identify how many Principal Components you need to build model
- Take spectra of the desired samples, understanding that there is no need for output (Y) information at this point
- Identify most prominent dimensions in the spectra by Row with Functional Principal Components from Functional Data Explorer
- Use Functional Principal Component Profiler to get an idea how your spectra are changing as variable of interest changes
- Save Functional Principal Component scores to data table to use in experimental design

- Create an experimental design using the functional Principal Components as factors (covariates)
- Run the experiment to gather the output of interest
- Model the results via PLS, and/or Generalized Regression (or other methods able to handle correlated factors)
- Determine the overall optimum solution
- Use this sustainable model to determine the outcome for all future samples that are analyzed using the same calibrated instrument
- Use Score plot to examine Categorical Data

Note: Q&A is included at times 24:21, 25:01, 37:01, 37:49, 38:34 and 42:08.