Combined Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA): an efficient chemometric approach in aged gel inks discrimination

Abstract The gel ink pen is the fastest growing pen class available on the modern market. Consequently, its prevalence in forensic casework is expected to increase. This poses a challenge to forensic scientists, since the chemistry of a gel pen ink differs to other commonly encountered inks; thus, discrimination of aged gel pen inks by traditional methods such as Thin Layer Chromatography (TLC) is limited and a new analytical methodology is required for distinguishing different formulations effectively. An objective multivariate statistical methodology (PCA and HCA) incorporating effective data pre-preprocessing (autoscaling) was developed and successfully applied to aged IR Spectroscopic data. Principal Component Analysis (PCA) revealed similar observations to Hierarchical Cluster Analaysis (HCA) and both techniques were more effective in distingushing the ink samples from all others Therefore, utilization of non-destructive analysis coupled with chemometrics techniques for discrimination of aged inks for forensic applications is supported.