Analyzing Soy meal in transmitance (part 6)

A good way to see the variance explained by the PCs is a 2D plot, where we see the projection of the scores over the PC terms, so it is a way to see in which PC term we can see a discrimination, or outliers.
In the case of the soy meal data, we can see the distribution of the scores in the plane formed by the first and second principal components.

 

and now imagine projecting the dots o perpendicularly over the axes (PCs), and this projections are the perpendicular dots of the next plot for the first and second PCs. In the projections of the first PC we see clearly out the samples 298 and 296, and if we would make a zoom of the second PC projections we would see clearly out the samples 373 and 298.

 

As we can see in this plot the whole variance of the data is explained by the first three PCs.