These two lines define a plane. If we rotate the axes, we can view the two lines orthogonally rather than obliquely , which will improve the color separation. A color angle rotated for orthogonal viewing. When we examine a plot of many dye concentrations in the principal components plane, we see that the different concentrations are like rays emanating from the origin. This suggests that the angular position of a point is the most useful variable for estimating dye concentration.
Redness denotes higher dye concentration.
Once the calibration is performed for a given soil, you can use the eigenvectors and regression equation to convert sample images to dye concentration. All of this can be done with our software, available free. RGBcal is for getting to the initial calibration equation. It reads in up to 1024 images of known dye concentrations at a time. Using all the points in these images, it calculates the eigenvectors, and displays the points in RGB, HSI, and principal components space.