10.1.   Single or Multiple Analyte Rankings

In principle, the variable selection of the frameworks can be performed for each analyte (response variable) separately or for all analytes together. For example, the variable selection by the parallel growing neural network was performed for each analyte separately in section 8.4.1 by using a separate ranking for both analytes, whereas the variable selection in the sections 9.1.2, 9.2.4, 9.3.2 and 9.3.3 was performed for all analytes together by using a combined ranking for all analytes together (addition of the individual rankings). In the second step of the iterative variable addition procedure, the combined ranking saves about n times the computing time for n analytes. Yet the question is, if a separate variable selection performs significantly better.

Thus, the ternary mixtures of methanol, ethanol and 1-propanol measured by SPR and evaluated by the parallel growing network framework (see section 9.2.4) were evaluated again using a separate ranking for each analyte and separate iterative addition steps (step 2 of the framework). The RMSE of the validation data, which are shown in table 10, are very similar and do not allow a clear decision which method to prefer.

table 10:    Relative RMSE in % for the prediction of the validation data measured by SPR and evaluated by the parallel growing neural network framework.

Evaluating the parallel growing network framework of the refrigerant data together instead of separately (compare with 4^th row of table 4 in chapter 8) resulted in slightly worse predictions of 2.08% for the validation data of R22 instead of 2.04% and slightly better predictions for R134a with 2.53% instead of 2.61%. For other data sets, also no clear preference can be seen rendering the more computing intensive separate variable selection superfluous.