Peer-Reviewed Journal Details
Mandatory Fields
Ryder, AG
Journal Of Forensic Sciences
Classification of narcotics in solid mixtures using Principal Component Analysis and Raman spectroscopy
Optional Fields
forensic science substance abuse detection classification Raman spectroscopy chemometrics narcotics Principal Component Analysis discrimination EXPLOSIVE MATERIALS QUANTITATIVE-ANALYSIS IN-SITU NEAR-IR IDENTIFICATION METHAMPHETAMINE SPECTROMETER ECSTASY TABLETS SPECTRA
Eighty-five solid samples consisting of illegal narcotics diluted with several different materials were analyzed by near-infrared (785 nm excitation) Raman spectroscopy. Principal Component Analysis (PCA) was employed to classify the samples according to narcotic type. The best sample discrimination was obtained by using the first derivative of the Raman spectra. Furthermore, restricting the spectral variables for PCA to 2 or 3% of the original spectral data according to the most intense peaks in the Raman spectrum of the pure narcotic resulted in a rapid discrimination method for classifying samples according to narcotic type. This method allows for the easy discrimination between cocaine, heroin, and MDMA mixtures even when the Raman spectra are complex or very similar. This approach of restricting the spectral variables also decreases the computational time by a factor of 30 (compared to the complete spectrum), making the methodology attractive for rapid automatic classification and identification of suspect materials.
Grant Details
Publication Themes
Informatics, Physical and Computational Sciences