Whenever possible I use LOCAL Models because they are quite easy to maintain. These are routine samples analyzed with the reference methods and plotted versus the predicted values. The statistics performs well and were into the error limits expected when the LOCAL Model was laoded for routine. There are small slope shifts, but these validation samples will be included in the database to improve the model and make it more robust.
User also analyzed some special pet-food samples which have much more moisture (red crosses) and we can se how the moisture performs well, but with a high bias.
Statistics for Ash (Cenizas)
Samples used for Statistics 81
Slope 0.962
Intercept 0.233
Bias -0.075
SEP 0.649
SEP(C) 0.649
RSQ 0.869
Statistics for Protein (Proteína)
Samples used for Statistics 98
Slope 1.011
Intercept 0.153
Bias 0.439
SEP 0.714
SEP(C) 0.566
RSQ 0.991
Statistics for Fat (Grasa)
Samples used for Statistics 83
Slope 0.915
Intercept 1.094
Bias 0.084
SEP 0.649
RSQ 0.975
Statistics for Moisture (Humedad)
Without red ones
Samples used for Statistics 116
Slope 1.021
Intercept -0.082
Bias 0.074
SEP 0.528
SEP(C) 0.525
RSQ 0.786
If you are interested in LOCAL Models you can click on the LOCAL label, to see other posts like:
Studing structure in LOCAL for validation
LOCAL: Batch Mode to select Max Number of Samples
LOCAL: Creating the RED files
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