8 dic. 2017

Previous steps for a LOCAL calibration.

With four different sets of Meat meal (4 species of pork), I develop a lib file for each one. I see one of them on the 3D graph and add the others as secondary files in order to see how they match one with the others. Looking to the correlation of the scores respect their own libraries it is clear that for all of them the moisture is the main source of variation and is explained in all in the first principal component. The second principal component is the highest correlated to the second principal component in the four libraries.
Three of the families set overlap almost in the protein range, but one of them had a broad range in the low protein, so the idea is to see this in the scores maps.
In this plot, we see the scores of the four sets in the PC space of one of the libraries, overlapped.
Dark blue:   Range of protein from 44,26 to 77,3
Green:        Range of protein from 69,50  to 79,51
Light blue:   Range of protein from 69,50  to 82,90
Red:            Range of protein from 66,51  to 87

If we see the map of scores, which contain the second principal component, for all the groups and the plot for the dark blue group divided in 3rds we can get some conclusions.
 This are previous studies in order to build a Local calibration, so more details will came in next posts.


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