Experimental variability and data pre-processing as factors affecting the discrimination power of some chemometric approaches (PCA, CA and a new algorithm based on linear regression) applied to (+/-)ESI/MS and RPLC/UV data: Application on green tea extracts.

The influence of the experimental variability (instrumental repeatability, instrumental intermediate precision and sample preparation variability) and data pre-processing (normalization, peak alignment, background subtraction) on the discrimination power of multivariate data analysis methods (Principal Component Analysis -PCA- and Cluster Analysis -CA-) as well as a new algorithm based on linear regression was studied. Data used in the study were obtained through positive or negative ion monitoring electrospray mass spectrometry (+/-ESI/MS) and reversed phase liquid chromatography/UV spectrometric detection (RPLC/UV) applied to green tea extracts. Extractions in ethanol and heated water infusion were used as sample preparation procedures. The multivariate methods were directly applied to mass spectra and chromatograms, involving strictly a holistic comparison of shapes, without assignment of any structural identity to compounds. An alternative data interpretation based on linear regression analysis mutually applied to data series is also discussed. Slopes, intercepts and correlation coefficients produced by the linear regression analysis applied on pairs of very large experimental data series successfully retain information resulting from high frequency instrumental acquisition rates, obviously better defining the profiles being compared. Consequently, each type of sample or comparison between samples produces in the Cartesian space an ellipsoidal volume defined by the normal variation intervals of the slope, intercept and correlation coefficient. Distances between volumes graphically illustrates (dis)similarities between compared data. The instrumental intermediate precision had the major effect on the discrimination power of the multivariate data analysis methods. Mass spectra produced through ionization from liquid state in atmospheric pressure conditions of bulk complex mixtures resulting from extracted materials of natural origins provided an excellent data basis for multivariate analysis methods, equivalent to data resulting from chromatographic separations. The alternative evaluation of very large data series based on linear regression analysis produced information equivalent to results obtained through application of PCA an CA.

Controlled drug delivery system based on ordered mesoporous silica matrices of captopril as angiotensin-converting enzyme inhibitor drug.

In the present study, captopril-loaded ordered mesoporous SBA-15 silica matrix were produced, functionalized, and characterized to obtain an efficient formulation of controlled drug delivery system. First, the starting SBA-15 materials are examined to verify that their synthesis has been successful considering the structural properties, using XRD, FTIR, and BET methods. Second, the influence of processing parameters of ordered mesoporous matrices for the loading and release of captopril was investigated. The release of captopril was controlled by tailoring the surface properties of the mesoporous silica via functionalization. The loading and release kinetics (in vitro in simulated gastric and intestinal fluids) showed that both of them were affected by the surface properties of the mesoporous silica materials. Such a formulation shows potential as an efficient controlled drug delivery system.