Evaluating Handheld Spectroscopic Techniques and Machine Learning Algorithms

Identifying cosmetics on substrates is essential in any crime scene processing. It is important in such cases to warrant the sample integrity and continuity. Spectroscopic techniques offer the advantage of analyzing samples non-destructively thus addressing the aforementioned requirements. When used in handheld modality, spectroscopic techniques offer rapid and on-site analysis with the ability to collect numerous datasets in minimal time. |Each spectrum is a unique fingerprint of the sample measured and that urges the need to collect large datasets and apply machine learning algorithms to make meaningful conclusions from analysis. Therefore, this work involved applying machine learning algorithms to infrared, near-infrared, and Raman spectroscopic data of cosmetics applied to different substrates. The cosmetics included powder, creams, lipsticks and nail polish; and the substrates included paper, fabric and glass. Machine learning algorithms were correlation method and principal component analysis. Both algorithms showed to be complementary in identifying cosmetics on different substrates, Thus, the approach proved accurate and precise for identifying substrates encountered at crime scenes.