The combination of furosemide (furo) and spironolactone (spiro) is very effective in the treatment of heart failure. In that case, maintaining good quality of these drugs in commercial tablets is must. Therefore, a simple, economical, precise and accurate method, i.e; chemometric assisted UV spectroscopy, for simultaneous determination of furosemide and spironolactone in combined dosage form has been developed. In this study, principal component regression (PCR) has been reported for this purpose. A calibration set of 36 mixture solutions containing furosemide and spironolactone in methanol in the concentration range of 2.0-12.0 µg/ml and 5.0-30.0 µg/ml respectively has been prepared by means of an orthogonal experimental design. The absorbance data for the concentration set have been obtained by direct measurement in UV spectrophotometer at 101 wavelength points in the spectral region of 200-300 nm for the zero order spectra. The chemometric technique is also successfully applied to available pharmaceutical formulations, tablets, with no interference from excipients. The analytical performances of principal component regression are characterized by relative prediction errors and recoveries (%). The good recoveries obtained in this case proved that the proposed chemometric technique could be applied efficiently in the quality control of the studied drugs simultaneously in their mixture as well as in the commercial dosage form with satisfactory precision and accuracy as alternative analysis tools.

There is a large variety and trademarks of vegetable oils in Bangladesh. The oils have characteristics very similar to each other and often cannot be classified by the observation of color, odor or taste. This paper proposes a vibrational spectroscopic method like FTIR in association with chemometric techniques to classify vegetable oils like: sunflower, mustard, sesame, soybean, castor, olive and palm oils from different manufacturers. In the FTIR spectra of oil, as information about fatty acid composition is concentrated in the range of 4400-200 cm-1 principal component analysis (PCA) was applied on the standardized full FTIR spectral data of this region for vegetable oils to totally capture the FTIR spectral pattern; seven varieties of vegetable oils could be successfully classified from their PCA scores. PCA of FTIR spectra of different known vegetable oils is used to determine the identity of several unknown vegetable oils. The unknowns are then analyzed, plotted, and identified based on their proximity to the known in principal component space. For the multivariate analysis PCA and soft independent modeling of class analogy (SIMCA) and support vector machines (SVMs) were used. 85% and 14% variability of data was explained by PC1 and PC2 respectively. PC1 has strong positive correlation with soybean, sunflower, palm and olive oil while strong negative correlation with mustard, castor and sesame oil. Soybean oils are positively and sesame oils are negatively correlated with PC2. Unknown oil samples can be identified properly by used supervised methods i.e. SIMCA, SVM by developing model with the help of PCA. The major interest of this method using chemometric analysis of spectral data is in their rapidity, since no chemical treatment of samples is required.