Unscrambler (Jam Exercise) - 002

PCR is a MLR regression, but indeed using X matrix, we use the T (scores) matrix. We know that the X explained variance is normally quite high for this first PC and decrease with the others, but in the PCR there is no guarantee that the explained variance for the Y follow that order and in the case of the post “Unscrambler (Jam Exercise) – 001”  is just 1% of explained variance for the first PC, 57% for the second and 34% for the third. This does not happen for the PLS.

Let´s develop a PLS1 regression for the same X (sensory) and Y (preference) than in “Unscrambler (Jam Exercise) – 001”.

We see how the first PLS term explain 91% of the variance, 3% the second and 2% the third.

The first term is very influence by parameters as Thickness which is inverse correlated with others which are preferenced by the consumers (Redness, Colour, Sweetness and Juiciness) . Other parameters as Chewiness, Bitterness, Rasp smell and flavor, do not have influence in the preferences of the consumers.

PLS terms 1 and 2, model quite well the groups for harvesting time H1, H2 and H3, which explain the important parameters for the customers. So,...., 2 terms seem enough for the model.

See in this link details from CAMO about this Jam data set: http://www.camo.com/products/unscrambler/trial.swf