On the discrimination between facial creams of different brands using Raman Spectroscopy and partial least squares discriminant analysis for forensic application.

Determining the origin of cosmetic traces is an important aspect of forensic investigations, that helps linking a suspect to a crime. Such type of evidence can help further narrow down the undergoing investigations. This paper reports the first use of Raman Spectroscopy (RS) coupled with the exploratory principal component analysis (PCA) and supervised partial least squares-discriminant analysis (PLS-DA) in facial creams. 40 facial cream samples of 8 different brands were studied in this work. Preliminary assessments through visual inspection of their Raman spectra revealed the presence of oxides, titanium dioxide, castor seed oil, and beeswax. Also, the peaks of alkyne groups were indicative of the presence of talc or mica compounds. The exploratory PCA correctly segregated the samples into 8 clusters and the supervised PLS-DA model correctly classified them into 8 classes. Further evaluation of the performance of the trained PLS-DA model resulted in perfect classification shown by the receiver operating characteristic (ROC) curves. The PLS-DA model also resulted in 100% accuracy of correctly assigning the brand on the face wipes on each of the five substrates viz. cotton, dry and wet tissue paper, nylon substrate, and polyester. This validation was done treating these samples as unknowns. The study has a potential for use under actual forensic casework conditions.