Artículos de revistas
Classical Least Squares Combined With Spectral Interval Selection Using Genetic Algorithm For Prediction Of Constituents In Pharmaceutical Solid Dosage Forms From Near Infrared Chemical Imaging Data
Registro en:
Journal Of Near Infrared Spectroscopy. N I R Publications, v. 24, p. 157 - 169, 2016.
0967-0335
1751-6552
WOS:000381677600007
10.1255/jnirs.1201
Autor
Alexandrino
Guilherme L.; Breitkreitz
Marcia C.; Poppi
Ronei J.
Institución
Resumen
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) A new algorithm that combines spectral interval selection using genetic algorithm and classical least squares (GA-iCLS) is presented for the prediction of the active pharmaceutical ingredients and excipients in various pharmaceutical solid dosage forms from near infrared chemical imaging data. The algorithm is based on the CLS approach, selecting the best wavenumber intervals in the unfolded hypercube of each sample (D), and in pure-compound reference spectra (S), wherein the pixel-to-pixel prediction capability of the compounds, obtained by C = DST(SST)(-1), is optimised for the samples. The wavelength intervals were selected (GA optimisation) while minimising the error between the mean concentrations of the ith compound predicted in the pixels and the nominal concentration in the corresponding sample (known a priori). The excluded wavenumber intervals from D (and S), for each sample, were interpreted based on systematic deviations from D = CST + E (CLS approach) due to the scattering effects and/or intermolecular interactions in mixtures of the pure compounds. The comparison of the chemical images generated from the predictions performed using the GA-iCLS algorithms with similar-images obtained without spectral interval selection, using direct CLS and multivariate curve resolution-alternating least squares, revealed the potential applicability of the proposed algorithm for analytical purposes for pharmaceuticals using chemical imaging data. 24 2 157 169 Brazil's National Council of Scientific and Technological Development (CNPq) Sao Paulo Research Foundation (FAPESP) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)