Periodic Table’s Properties Using Unsupervised Chemometric Methods: Undergraduate Analytical Chemistry Laboratory Exercise

An application of unsupervised chemometrics methods using
periodic
table properties for an analytical chemistry laboratory exercise is
presented. Chemometric techniques such as hierarchical clustering
analysis (HCA), k-means, and principal component analysis (PCA) were
applied to a multivariate data set of chemical properties of the elements
(atomic radius, electronegativity, ionization energy, electronic affinity,
thermal conductivity, density, entropy, and specific heat). The exercise
was carried out by undergraduate students attending a chemometric
analysis class during the fifth semester of their third year at our
educational institution. The theory of HCA, k-means, and PCA is discussed,
and multivariate analysis procedures (data set construction, preprocessing,
dendrogram, k-means clustering, scores, and loadings) were carried
out using the well-liked programming language R, a widely used programming
language designed for data analysis and statistics, within the user-friendly
RStudio integrated development environment. The unsupervised algorithms
were able to find “natural” clustering from the periodic
table using the data structure without any prior knowledge of the
class assignment of the samples. This analytical chemistry laboratory
exercise with chemometric techniques can also be used in a wide range
of laboratory activities such as water analysis, food analysis, drug
analysis, and physical chemistry.