Discerning Some Tylenol Brands Using Attenuated Total Reflection Fourier Transform Infrared Data and Multivariate Analysis Techniques
Chemistry and Biochemistry
Principal component anal. (PCA) and partial least squares discriminant anal. (PLS-DA) were used to classify acetaminophen-contg. medicines using their attenuated total reflection Fourier transform IR (ATR-FT-IR) spectra. Four formulations of Tylenol (Arthritis Pain Relief, Extra Strength Pain Relief, 8 H Pain Relief, and Extra Strength Pain Relief Rapid Release) along with 98% pure acetaminophen were selected for this study because of the similarity of their spectral features, with correlation coeffs. ranging from 0.9857 to 0.9988. Before acquiring spectra for the predictor matrix, the effects on spectral precision with respect to sample particle size (detd. by sieve size opening), force gauge of the ATR accessory, sample reloading, and between-tablet variation were examd. Spectra were baseline cor. and normalized to unity before multivariate anal. Anal. of variance (ANOVA) was used to study spectral precision. The large particles (35 mesh) showed large variance between spectra, while fine particles (120 mesh) indicated good spectral precision based on the F-test. Force gauge setting did not significantly affect precision. Sample reloading using the fine particle size and a const. force gauge setting of 50 units also did not compromise precision. Based on these observations, data acquisition for the predictor matrix was carried out with the fine particles (sieve size opening of 120 mesh) at a const. force gauge setting of 50 units. After removing outliers, PCA successfully classified the 5 samples in the first and second components, accounting for 45.0% and 24.5% of the variances, resp. The 4-component PLS-DA model (R2 = 0.925 and Q2 = 0.906) gave good test spectra predictions with an overall av. of 0.961 ± 7.1% RSD vs. the expected 1.0 prediction for the 20 test spectra used.