When we check a model with a validation set, what we normally look is to the standard error of prediction (RMSEP) and the RSQ.
After we check if we have a bias and to the standard error of prediction corrected by bias. Maybe we are happy with the results if we see that are similar to the calibration statistics or maybe not so happy and think that the calibration does not work.
It is important to check if we have outliers and to remove those samples that could increase the errors, but if they are not clear outliers, they must stay. What we can do is if the error is bigger is some parts of the XY plot.
Maybe the calibration is not so fine for certain range, but it works fine for other range. This way we can take some conclusion about where to include more samples to improve the calibration.
Histograms will help you with this as well, but also the X-Y plot of reference versus predicted. In the case of flour there are different types according to the W parameter.
In the X-Y plot I can see with an independent validation set that the calibration is performing well for flour between 200 and 270 of W, with a RMSEP of 9. This tells me that the calibration is working fine for this type of flour used normally for pizza products.
The calibration does not work for soft flour (low W) or hard flour (high W). You have to decide how to improve it or to separate the flour product into 3 products in order to improve the predictions.
Look always to the plots and try to find conclusions about the data.