Principal component analysis (PCA)
is one of the most important
and powerful methods in chemometrics as well as in a wealth of other
areas. Running a PCA results in two main elements, the score plot
and the loading plot; the score plot provides the location of the
samples, and the loading plot indicates correlations among variables,
the trends in the grouping of samples, and the most important variables.
In the past 10 years teaching chemometrics, we have struggled with
not having free software with an easy to use graphical user interface
for data handling and calculations. In this paper, we provide a series
of examples that students used to carry PCA in R-Project, a free and
open source software program. In the first example, students used
PCA to find correlations among chemical properties of chemical elements
and relate these properties with the periodic distribution of the
elements. In the second example, meat samples were grouped using 14
variables, and students could observe how outlier samples might influence
the PCA model; in this case, they were also taught how to use the t test to choose the variables that were significant to
the PCA model. In the third example, healthy patients were differentiated
from diabetic patients using 163 lipid concentrations. In the fourth
example, Atlantic salmon samples were differentiated from catfish
samples. In the fifth and sixth examples, students were able to observe
how data treatment affects the classification of natural products
and edible oils, respectively.