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).

Variable selection, outlier detection, and figures of merit estimation in a partial least-squares regression multivariate calibration model. A case study for the determination of quality parameters in the alcohol industry by near-infrared spectroscopy.

Practical implementation of multivariate calibration models has been limited in several areas due to the requirement of appropriate development and validation to prove their performance to standardization agencies. Herein, a detailed description of the application of multivariate calibration based on partial least-squares regression models (PLSR) for the determination of soluble solids (BRIX), polarizable sugars (POL), and reducing sugars (RS) in sugar cane juice, based on near infrared spectroscopy (NIR), for the alcohol industries is presented. The development of the models, including variable selection and outlier elimination, and their validation by determination of figures of merit, such as accuracy, precision, sensitivity, analytical sensitivity, prediction intervals, and limits of detection and quantification, are described for a representative data set of 1381 sugar cane samples.

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.

Figures of merit for the determination of the polymorphic purity of carbamazepine by infrared 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.