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Promising Four multivariate methods like CLS (Classical least square), ILS (Inverse least square), PCR (Principle component regression) and PLSR (Partial least square regression) were used for the determination of ternary mixture of Clidinium Bromide (CDB), Dicyclomine Hydrochloride (DICY) and Chlordiazepoxide (CDZ) in synthetic and market formulation. Overlapped data was quantitatively resolved by using chemometrics methods, viz CLS, ILS and PLSR methods. Calibrations sets were constructed by means of the absorption data matrix corresponding to the concentration data matrix. A prediction set design of the concentration data corresponding to the CDZ, DICY and CDB mixtures was prearranged statistically to maximize the information content from the spectra and to minimize the error of multivariate calibrations. By applying the respective algorithms for CLS, PLSR, PCR and ILS to the measured spectra of the calibration set, an appropriate model was obtained. This model was selected on the basis of % RSEP and % mean recovery values and the same was applied to the prediction set and tablet formulation. Mean recoveries of the marketed formulation set together with the figures of merit (calibration sensitivity, selectivity, limit of detection, limit of quantification and analytical sensitivity) were estimated. Validation of the proposed methods was successfully assessed for analysis of drugs in the various prepared synthetic mixtures and marketed formulation.
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Author(s): Kumar N Bansal A Lalotra R Sarma GS Rawal Rk
Root mean squares error of cross-validation, Figures of merit, Classical least square, Inverse least square, Partial least square regression, Multivariate