Residual Variance in principal component space qualitative model

Hi  All
I developed a qualitative identification method (Library) to identify pharmaceutical finished product (Capsules).
In the library there are two products :the product itself 479 spectra, the product placebo 16 spectra.
The pattern recognition method used is "Residual Variance in principal component space"
The match value used: Probability level: 0.95 (Samples with lower probability than 0.95 pass identification)
Pretreatment (SNV+2^nd derivative)
The problem is that each time I deploy my method the probability values obtained immediately increase and much higher values are obtained during routine use than during internal validation. (See picture added)
The black,  red and green points show the values for internal validation for the model updated with the spectra from routine analysis
Can any of you out there think of acause for this
Thanks
 
 
.