Jez Willian B. Braga

Estado da arte de figuras de mérito em calibração multivariada

The validation of an analytical procedure must be certified through the determination of parameters known as figures of merit. For first order data, the acuracy, precision, robustness and bias is similar to the methods of univariate calibration. Linearity, sensitivity, signal to noise ratio, adjustment, selectivity and confidence intervals need different approaches, specific for multivariate data. Selectivity and signal to noise ratio are more critical and they only can be estimated by means of the calculation of the net analyte signal. In second order calibration, some differentes approaches are necessary due to data structure.

Validation of multivariate calibration models in the determination of sugar cane quality parameters by near infrared spectroscopy

The determination of soluble solids (BRIX), polarizable sugars (POL) and reducing sugars (RS) sugar cane juice by using near infrared spectroscopy (NIR) and multivariate calibration was developed and validated by calculation of figures of merit. Due to the heterogeneity of the samples, first it was necessary to optimize the calibration set by outlier elimination. The figures of merit such as: sensitivity, analytical sensitivity, selectivity, confidence limit, precision (mean, repeatability), accuracy and signal-to-noise ratio were calculated. Feasible results were obtained for BRIX and POL with RMSEP values of 0.28 and 0.42% of juice and precision of 0.02 and 0.08% of juice, respectively. For both BRIX and POL goodness of fit showed correlation coefficients of 0.99. These results indicate that the models developed for BRIX and POL can be used as an alternative to standard procedures in the sugar cane industry.

Validação de modelos de calibração multivariada: uma aplicação na determinação de pureza polimórfica de carbamazepina por espectroscopia no infravermelho próximo

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.

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.

Detection of Counterfeit Durateston® Using Fourier Transform Infrared Spectroscopy and Partial Least Squares - Discriminant Analysis

Medicines containing anabolic steroids are one of the main targets for counterfeiting worldwide, including Brazil. The aim of this work was to propose a method for discriminating original and counterfeit Durateston® ampoules by Fourier transform infrared spectroscopy (FTIR) followed by chemometric analysis. Ninety-six ampoules of Durateston®, 49 originals and 47 counterfeits, were analyzed by gas chromatography with mass spectrometry (GC-MS) and by FTIR. Principal component analysis was applied to the infrared spectra to detected different clusters, corresponding to original samples and different types of counterfeits. A partial least squares - discriminant analysis method was proposed to discriminate original samples from those counterfeits that were indistinguishable from the originals in the infrared analysis. A training subset comprised of one-third of the available spectra was used to establish a suitable model that correctly discriminated all samples in the test subset, resulting in 0% of false positive or negative results and 100% of efficiency rate, sensitivity and specificity. In addition to the low cost of the infrared technique, the proposed method is fast, reliable and suitable to replace GC-MS methods used in Durateston® ampoule analyses to detect counterfeiting.