why does preprocessed test data change when calibration dataset (and model based on that data) changes? i have spectral, normalized datasets, the preprocessing was 1. derivative + autoscale.

for example: i have 2 datasets: 100 samples with 400 variables each as calibration data; 20 samples for test data. preprocessing as above, data was cross-validated as well. the model for PLS-DA was calculated using pls toolbox in matlab. when i plot preprocessed test data i get different results compared to situation when i used e.g. half of those calibration-set samples to create a model. and i can't plot preprocessed test data without calculating the model. how is it connected?

i also tried to put my 20-sample test data as calibration data just to create model, and the preprocessed data plot was also different from plots obtained before.

sorry for my eng and messy presentation of the problem. i'll explain more in comments if necessary.

Why does preprocessed test data change when calibration dataset (and model based on that data) changes? I have spectral, normalized datasets, the preprocessing was 1. derivative + autoscale.

For example: I have 2 datasets: 100 samples with 400 variables each as calibration data; 20 samples for test data. preprocessing as above, data was cross-validated as well. the model for PLS-DA was calculated using pls toolbox in matlab. when I plot preprocessed test data I get different results compared to situation when I used e.g. half of those calibration-set samples to create a model. and I can't plot preprocessed test data without calculating the model. how is it connected?

I also tried to put my 20-sample test data as calibration data just to create model, and the preprocessed data plot was also different from plots obtained before.