Qualitative analysis using Raman spectroscopy and chemometrics: a comprehensive model system for narcotics analysis.

The rapid, on-site identification of illicit narcotics, such as cocaine, is hindered by the diverse nature of the samples, which can contain a large variety of materials in a wide concentration range. This sample variance has a very strong influence on the analytical methodologies that can be utilized and in general prevents the widespread use of quantitative analysis of illicit narcotics on a routine basis. Raman spectroscopy, coupled with chemometric methods, can be used for in situ qualitative and quantitative analysis of illicit narcotics; however, careful consideration must be given to dealing with the extensive variety of sample types. To assess the efficacy of combining Raman spectroscopy and chemometrics for the identification of a target analyte under real-world conditions, a large-scale model sample system (633 samples) using a target (acetaminophen) mixed with a wide variety of excipients was created. Materials that exhibit problematic factors such as fluorescence, variable Raman scattering intensities, and extensive peak overlap were included to challenge the efficacy of chemometric data preprocessing and classification methods. In contrast to spectral matching analyte identification approaches, we have taken a chemometric classification model-based approach to account for the wide variances in spectral data. The first derivative of the Raman spectra from the fingerprint region (750-1900 cm(-1)) yielded the best classifications. Using a robust segmented cross-validation method, correct classification rates of better than ∼90% could be attained with regression-based classification, compared to ∼35% for SIMCA. This study demonstrates that even with very high degrees of sample variance, as evidenced by dramatic changes in Raman spectra, it is possible to obtain reasonably reliable identification using a combination of Raman spectroscopy and chemometrics. The model sample set can now be used to validate more advanced chemometric or machine learning algorithms being developed for the identification of analytes such as illicit narcotics.