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.