Looking to the Residuals Plot.

Even if an application recommend us to make a type of adjustment, we have to look always to the plots.

In this case due to 3 samples (with prediction higher than 30) which are bellow the zero residual line, the application recommend us to adjust the slope, 

but it is clearly seen that for the rest of the samples the problem is a clear Bias problem. So we remove these samples with a prediction higher than 30 from the original data frame:

pass1<-subset(pass1$Table1,pass1$Table1[,3]<30)

and now the application will recommend the adjustment of the Bias. It is clear that we need more samples of this type in the plot to see clearly if we have a 

problem in the prediction of the protein content in this case.

The red dots are the sample values without bias adjustment and the yellow ones the samples with bias adjustments. The distribution of the yellow dots is fine 

and represents the random noise.

It is clear that more work must be done on the calibration or in the analysis of 

the reference methods in order to improve the predictions.

Validation Samples  = 85 
Reference Mean  = 21.7 
Predicted Mean  = 20.1 
RMSEP           : 2.35 (Current Error)
Bias            : 1.59 (Sugested Bias correction)
SEP             : 1.74 (Random error once Bias is corrected)