Multivariate curve resolution of pH gradient flow injection mixture analysis with correction of the Schlieren effect

Multivariate curve resolution using alternating least squares (MCR-ALS) was used to quantify ascorbic (AA) and acetylsalicylic (ASA) acids in four pharmaceutical samples using a flow injection analysis (FIA) system with pH gradient and a diode array (DAD) spectrometer as a detector. Four different pharmaceutical drugs were analyzed, giving a data array of dimensions 51 x 291 x 61, corresponding respectively to number of samples, FIA times and spectral wavelengths. MCR-ALS was applied to these large data sets using different constraints to have optimal resolution and optimal quantitative estimations of the two analytes (AA and ASA). Since both analytes give an acid-basic pair of species contributing to the UV recorded signal, at least four components sholuld be proposed to model AA and ASA in synthetic mixture samples. Moreover, one additional component was needed to resolve accurately the Schlieren effect and another additional component was also needed to model the presence of possible interferences (like caffeine) in the commercial drugs tablets, giving therefore a total number of 6 independent components needed. The best quantification relative errors were around 2% compared to the reference values obtained by HPLC and by the oxidation-reduction titrimetric method, for ASA and AA respectively. In this work, the application of MCR-ALS allowed for the first time the full resolution of the FIA diffusion profile due to the Schlieren effect as an independent signal contribution, suggesting that the proposed MCR-ALS method allows for its accurate correction in FIA-DAD systems.133677478

Comparison of variance sources and confidence limits in two PLSR models for determination of the polymorphic purity of carbamazepine

This paper presents a study of the variance sources and confidence limits in two PLSR models for the determination of the polymorphic purity of Carbamazepine, using near and mid infrared spectroscopy. The variance sources estimated and compared were reference values, instrumental responses and fit of the model. The variance of instrumental responses was estimated experimentally and theoretically, and the differences were discussed. The confidence limits at three confidence levels: 95%, 90% and 50% were determined, presenting a good agreement with the expected values. The predictive ability of the models was compared, showing that both present the same overall performances with RMSEP of 0.67% for near infrared and 0.62% for mid infrared spectroscopy. It was also verified that, for both models, the main variance source remains the error of the PLSR model. (c) 2005 Elsevier B.V. All rights reserved.801505

Figures of merit for the determination of the polymorphic purity of carbamazepone by infared spectroscopy and multivariate calibration

Polymorphism is an important property in the quality control of pharmaceutical products. In this regard, partial least squares regression and the net analytical signal were used to build and validate a multivariate calibration model using diffuse reflectance infrared spectroscopy in the region of 900-1100 cm(-1) for the determination of the polymorphic purity of carbamazepine. Physical mixtures of the polymorphs were made by weight, from 80 to 100% (w/w) form III mixed with form I. Figures of merit, such as sensitivity, analytical sensitivity, selectivity, confidence limits, precision (mean, repeatability, intermediate), accuracy, and signal-to-noise ratio were calculated. Feasible results were obtained with maximum absolute error of 2% and an average error of 0.53%, indicating that the proposed methodology can be used by the pharmaceutical industry as an alternative to the X-ray diffraction (United States Pharmacopoeia method). (C) 2004 Wiley-Liss, Inc.9382124213

Validation of models of multivariate calibration: An application in the determination of polymorphic purity of carbamazepine by near infrared spectroscopy.

The application of analytical procedures based on multivariate calibration models has been limited in several areas due to requirements of validation and certification of the model. Procedures for validation are presented based on the determination of figures of merit, such as precision (mean, repeatability, intermediate), accuracy, sensitivity, analytical sensitivity, selectivity, signal-to-noise ratio and confidence intervals for PLS models. An example is discussed of a model for polymorphic purity control of carbamazepine by NIR diffuse reflectance spectroscopy. The results show that multivariate calibration models can be validated to fulfill the requirements imposed by industry and standardization agencies.2761004101

Evaluation of the number of factors needed for residual bilinearization in BLLS and UPLS models to achieve the second-order advantage

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Bilinear least squares (BLLS) and unfold partial least squares (UPLS) are second-order multivariate calibration methods, which require the application of the residual bilinearization (RBL) algorithm to achieve the second-order advantage. The present work presents a study of the choice of the number of RBL factors, in BLLS and UPLS models, for two different datasets based on fluorescence and flow injection analysis (FIA) measurements. Confidence limits for the noise level and mean calibration residuals, based on a student-t distribution, are proposed as a criterion for determination of the number of RBL factors. Feasible results were obtained based on the proposed confidence limits, but divergences were observed in some situations in the FIA dataset due to either differences in the models or characteristics of the analyte signal. These results suggest, whenever possible, that the number of RBL factors should be checked with a dataset composed by samples where values of the property of interest are known from a reference method. (C) 2009 Elsevier B.V. All rights reserved.100299109UNICAMP-Graduate Instructors ProgramFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

Determination of pesticides and metabolites in wine by high performance liquid chromatography and second-order calibration methods

The models parallel factor analysis (PARAFAC) and the recently introduced bilinear least squares (BLLS) were applied to develop second-order calibration methods to high performance liquid chromatography with diode array detection (HPLC-DAD) data, where overlap of interferences with the compounds of interest was observed, making the determination and resolution of the analytes possible. In this work, the simultaneous determination of five pesticides and two metabolites in wine samples by HPLC-DAD was performed, using the second-order advantage. The results of two chromatographic methods were compared, involving either isocratic or gradient elution. An appropriate preprocessing method was necessary to correct the effects of time shifts, baseline variations and background. BLLS presented results that were of the same quality as PARAFAC in five cases, but in two other situations only PARAFAC enabled analyte quantitation. Relative errors of prediction lower than 10% for all compounds were obtained, indicating that the methodology employing HLPC-DAD and second-order calibration can handle complex analytical systems. (C) 2007 Elsevier B.V. All rights reserved.1148220021

Measurement of Crystalline Silica Aerosol Using Quantum Cascade Laser–Based Infrared Spectroscopy

Abstract Inhalation exposure to airborne respirable crystalline silica (RCS) poses major health risks in many industrial environments. There is a need for new sensitive instruments and methods for in-field or near real-time measurement of crystalline silica aerosol. The objective of this study was to develop an approach, using quantum cascade laser (QCL)-based infrared spectroscopy (IR), to quantify airborne concentrations of RCS. Three sampling methods were investigated for their potential for effective coupling with QCL-based transmittance measurements: (i) conventional aerosol filter collection, (ii) focused spot sample collection directly from the aerosol phase, and (iii) dried spot obtained from deposition of liquid suspensions. Spectral analysis methods were developed to obtain IR spectra from the collected particulate samples in the range 750–1030 cm−1. The new instrument was calibrated and the results were compared with standardized methods based on Fourier transform infrared (FTIR) spectrometry. Results show that significantly lower detection limits for RCS (≈330 ng), compared to conventional infrared methods, could be achieved with effective microconcentration and careful coupling of the particulate sample with the QCL beam. These results offer promise for further development of sensitive filter-based laboratory methods and portable sensors for near real-time measurement of crystalline silica aerosol