Use JMP Pro to build a sustainable empirical model based on spectral data/wavelengths.

See how to:

• 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.