Comparison of Derivative Preprocessing and Automated Polynomial Baseline Correction Method for Classification and Quantification of Narcotics in Solid Mixtures

Abstract

This work offers a real-world comparison of derivative preprocessing and a new polynomial method described by Lieber and Mahadevan-Jansen (LMJ) for baseline correction of Raman spectra with widely varying backgrounds. This comparison is based on their outcomes in factor analysis, analyte discrimination, and quantification. Both correction methods are applied to a Raman spectra data set taken from 85 solid samples of illegal narcotics diluted with various materials. It is found that neither approach outperforms the other, as they give similar principal component analysis (PCA) models and quantification errors: cocaine and heroin show cross-validation errors of approximately 8%, while MDMA is quantified to a cross-validation error of approximately 3–4%. The LMJ method does offer several other advantages, the most significant being the retention of original peak shapes after the correction, which simplifies the interpretation of the preprocessed spectra. The LMJ method is therefore recommended for use as a baseline correction method in future research with Raman spectroscopy.