Spectroscopy Letters · 2011 · 20 citations · 21 references
PharmacyPharmaceutical AnalysisToxicologyAnalytical ChemistryAnalytical ToxicologySpectroscopic AnalysisInstrumental AnalysisRaman SpectroscopySpectral Data ProcessingSpectroscopic Method ValidationPartial Least Squares RegressionQuantitative Raman SpectroscopyRaman Data AnalysisSeized Drug AnalysisSmoothingSpectral Data PreprocessingSpectral PreprocessingSpectroscopic Drug Analysis
ABSTRACT Modern drug laws require that a seized sample be characterized for both the illegal substances present and the quantity of each of those substances. The goal of this work was to develop a common approach to model development based on Raman spectroscopic analysis followed by partial least squares (PLS) regression that would allow us to obtain quantitative information from simulated street-drug samples. Each drug sample contained one drug surrogate—either isoxsuprine, norephedrine, benzocaine, or lidocaine—and up to 3 different cutting agents. All spectra were acquired on a homebuilt Raman instrument equipped with a rotating sample holder. The same steps were employed for developing separate models for each drug surrogate, including spectral preprocessing by Savitsky-Golay smoothing, differentiation, mean-centering, and autoscaling. PLS models were developed using 2 latent variables that yielded root mean square errors of calibration (RMSEC) values in the 3% range and root mean square error of prediction (RMSEP) values in the 4% range.
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