Updating the equation

We have new data to update the calibration for a pet food category.

 This data comes has been kept from different validations, and we decide that is time to update the calibration from different reasons (we have new variability: new batches, new formulation,….).

After looking at the spectra (searching for X outliers).We divide again the data set into validation and calibration sets, an develop different calibrations for each constituents in search of the best math treatment, more or less terms,….., which treatment take out less outliers, which more,….

Take notes, find answers and conclusions:

This is an example for the protein:

Important to look at the SEV (standard error of validation) we get for the Validation Set chosen randomly, and the other statistics.

Think that in a normal case:

SEC <  SECV < SEV
After this study, mix again validation and calibration samples and develop the calibration (for each constituent) with the settings you found better. Probably this option increase change the number of terms, and of course will change the statistics.

After all the calibrations are developed, compare the new statistics with the ones of the previous equations.

Statistics can improve or even to be worse. Think why:

Look at the Mean and Std. Dev.     

In this case I´m quite happy because I got a good improvement in the ash constituent. But the others have new variability and more samples and that is also very important.

Install the calibration in routine, and store new samples for the next validation.

Make a control chart (residual vs. lab value) meanwhile to control the calibration.

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