to the previous chapters, it has been demonstrated that both, the genetic algorithm
framework and the parallel growing neural network framework are superior to
the common methods of multivariate calibration in terms of calibration quality.
Both frameworks allow a reproducible variable selection resulting in smaller
optimized neural network models with a better generalization ability compared
with common neural networks. Both frameworks show reproducible and robust results
whereby the parallel growing neural network framework seems to be more robust
and shows a slightly better generalization ability, which has also been observed
for the binary mixtures of the refrigerants. The classical chemometric methods
like the PLS and INLR once more have problems to model the nonlinear relationships
of the data similar to chapter 6. It was also shown that
the PCA-NN could not deal with changes of the noise in the data resulting in
significant biases of the predictions.