This research was initiated as part of the fight against public health problems of rising counterfeit, substandard and poor quality medicines and herbal products. An effective screening strategy using a two-step combination approach of an incremental near infrared spectral database (step 1) followed by principal component analysis (step 2) was developed to overcome the limitations of current procedures for the identification of medicines by near infrared spectroscopy which rely on the direct comparison of the unknown spectra to spectra of reference samples or products. The near infrared spectral database consisted of almost 4000 spectra from different types of medicines acquired and stored in the database throughout the study. The spectra of the test samples (pharmaceutical and herbal formulations) were initially compared to the reference spectra of common medicines from the database using a correlation algorithm. Complementary similarity assessment of the spectra was conducted based on the observation of the principal component analysis score plot. The validation of the approach was achieved by the analysis of known counterfeit Viagra samples, as the spectra did not fully match with the spectra of samples from reliable sources and did not cluster together in the principal component analysis score plot. Pre-screening analysis of an herbal formulation (Pronoton) showed similarity with a product containing sildenafil citrate in the database. This finding supported by principal component analysis has indicated that the product was adulterated. The identification of a sildenafil analogue, hydroxythiohomosildenafil, was achieved by mass spectrometry and Nuclear Magnetic Resonance (NMR) analyses. This approach proved to be a suitable technique for quick, simple and cost-effective pre-screening of products for guiding the analysis of pharmaceutical and herbal formulations in the quest for the identification of potential adulterants.
- adulterated herbal supplements
- counterfeit medicine
- principal component analysis
- spectral database