Colón González, Francheska M.
Loading...
1 results
Publication Search Results
Now showing 1 - 1 of 1
Publication Detection and discrimination of high explosives on human hair by Raman scattering(2020-05-29) Colón González, Francheska M.; Hernández Rivera, Samuel P.; College of Arts and Sciences - Sciences; Parés Matos, Elsie I.; Ríos Velázquez, Carlos; Department of Chemistry; Figueroa Medina, Carmen I.High explosives (HE) represent a high risk to the safety and health of the general population. Therefore, there is an ongoing demand for methods of analysis with limits of detection at trace levels for these hazardous chemicals. Since human hair is one of the main physical attributes of our bodies, its interaction with explosives can be of critical importance in forensic applications. Noninvasive in situ methods of elucidating these interactions, such as spectroscopic methods, are preferred, and among these Raman Scattering (RS) is overwhelmingly favored. Accordingly, this study is based on the detection of the HEs 2,4,6-trinitrotoluene (TNT), 1,3,5- trinitroperhydro-1,3,5-triazine (RDX), and pentaerythritol tetranitrate (PETN) on human hair strands by RS. Raman spectral libraries obtained with a 660 nm laser line were created for black, bleached, and entirely grey hair using direct deposition of PETN, TNT, and RDX. Spectral data were preprocessed to correct a high fluorescence background exhibited by the samples due to the indole groups and melanin present in hair. Despite the high fluorescence levels that characterized all samples, the vibrational signatures that identify the presence of the HE studied could be detected once the best acquisition parameters were established. Among the samples of the three hair types, grey hair was the best substrate to observe the vibrations of HE on hair. Multivariate analysis of explosives on hair demonstrated that using principal components analysis (PCA), it was possible to discriminate the HEs signals from those of the substrates (hair types) on black, grey, and bleached hair by monitoring characteristic peaks for the nitro group's vibrations of the explosives. Grey hair presented good discrimination for the explosives due to the absence of melanin. The best modes for the discrimination of HEs from all three types of hair were based on PCA, using algorithms of the first and second derivative as pre-treatment of the data. iii Predictions models as Projections and SIMCA resulted in an excellent way to classify a new set of data for unknown samples of explosives/black hair. The classifications were based on the more substantial variation on the NO2 symmetric vibration for each HE. Keywords: high explosives (HE), human hair, Raman scattering (RS), forensic science, multivariate analysis