Resolution of overlapping capillary electrophoresis peaks by using chemometric analysis: Quantification of the components in compound reserpine tablets

Angle Distribution of Loading Subspace (ADLS) for estimating chemical rank in multivariate analysis: Applications in spectroscopy and chromatography

Multivariate analyses are increasingly popular to explore the underlying structure of multivariate datasets, which are more and more prevalent in analytical chemistry. However, difficulties can be associated with estimating the number of components for the data with considerable coherence and noise. The method of Angle Distribution of Loading Subspace (ADLS) has been proposed to estimate the number of components for Principal Component Analysis (PCA) and PARAllel FACtor analysis (PARAFAC), which showed some advantages, in particular in the case of datasets with high coherence, over the commonly used methods (scree plot and cross-validation in PCA, and core consistency diagnostics (CORCONDIA) in PARAFAC). In this paper, we systematically improved and applied ADLS to estimate the number of components in different multivariate methods including, Multivariate Curve Resolution (MCR), PARAFAC and four-way PARAFAC. Firstly, we showed that ADLS performed better when estimating the chemical rank for MCR analysis, compared with scree plots. As well as this, we improved ADLS in multi-way analysis (three- and four-way PARAFAC) by calculating the loading subspace in advance using the Khatri-Rao product. The improved ADLS in multi-way analysis provided the correct result for the simulated three-way fluorescence datasets with unevenly distributed coherence at different dimensions, while the previous version of ADLS showed biased results and CORCONDIA / split-half analysis provided relatively unstable results. Moreover, ADLS was used to estimate the chemical rank for a four-way real-life fluorescence dataset analyzed by four-way PARAFAC. In this case the result of chemical rank results from ADLS was more precise and informative compared with CORCONDIA /split-half analysis in four-way analysis.

Application of artificial neural networks in the prediction of product distribution in electrophoretically mediated microanalysis

The successful application of artificial neural networks toward the prediction of product distribution in electrophoretically mediated microanalysis is presented. To illustrate this concept, we examined the factors and levels required for optimization of reaction conditions for the conversion of nicotinamide adenine dinucleotide to nicotinamide adenine dinucleotide, reduced form by glucose-6-phosphate dehydrogenase in the conversion of glucose-6-phosphate to 6-phosphogluconate. A full factorial experimental design examining the factors voltage, enzyme concentration, and mixing time of reaction was utilized as input-output data sources for suitable artificial neural networks training for prediction purposes. This approach proved successful in predicting optimal values in a reduced number of experiments. Model validation addressing the extent of reaction and product ratios were subsequently determined experimentally in replicate analyses, with results shown to be in good agreement (<10% discrepancy difference) with predicted data.

Alternative calibration approaches for LC–MS quantitative determination of coeluted compounds in complex environmental mixtures using multivariate curve resolution

Different calibration approaches including external calibration, standard addition and internal standard are evaluated for quantification of coeluted compounds in liquid chromatography with MS spectrometry detection in scan mode and using multivariate curve resolution. These different calibration approaches are proposed to cope with sensitivity changes and matrix effects encountered in the analysis of complex natural environmental samples. By using them, multivariate curve resolution analysis of MS data in scan mode gave similar quantitative results to those obtained by LC-MS in selected ion monitoring (SIM) mode (in both cases errors were below 16% for internal standard combined with standard addition strategy), and it provided at the same time a means of analyte confirmation via their resolved pure MS spectra, and a means to gather a larger amount of information about the whole chromatographic process and to facilitate the simultaneous determination of multiple analytes in the same chromatographic run using the same experimental and instrumental conditions.

Determination of sub-ppb reserpine by an optosensing device based on photochemically induced fluorescence

A flow injection-solid-phase spectroscopy (FI-SPS) system implemented with photochemically induced fluorescence (PIF) is described for the rapid and very sensitive determination of reserpine in biological fluids and pharmaceutical formulations. An intensively fluorescent photoproduct is in-line generated, retained on C18 silica gel in the detection area and monitored at 394/489 nm (lambdaex/lambdaem). After the establishment of the appropriate working variables, the system is calibrated at two different injection volumes, 100 and 800 microL, achieving detection limits of 0.33 and 0.05 ng mL(-1), respectively. The RSD for reserpine at 2 ng mL(-1) (800 microL) was 1.5% (n=10). The sampling rates were 46 and 43 h(-1) for each injection volume, respectively. The potential interference of some common species coexisting with reserpine in the analysed samples was also studied. The procedure was successfully applied to commercial formulations, urine and serum without any previous treatment of samples. Recoveries ranged from 94.9 to 100.2%.