The best way to check the performance of a calibration is with a new time based validation set. In this case one calibration has been developed to predict pH in cocoa paste. The calibration has been developed choosing the best performance with a cross validation using groups.
After this the model has been installed in routine and after 2 months, new samples with reference values attached have been collected so we can run the validation to see the statistics. This is the performance:
One of the sample with a high GH is classified as outlier, so we can think that it was a lab error value and we can excluded without a good reason and that must not be done.
Why not to develop again the model with the old training set trying to find a better math-treatment or configuration and validate with this new time based set. The results are surprising with a "None-0-0-1-1" math and 15 PLS terms:
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