Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/326995
Type: Artigo
Title: 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
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
Author: Alexandrino, Guilherme L.
Breitkreitz, Márcia C.
Poppi, Ronei J.
Abstract: 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.
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 fr
Subject: Near Infrared Chemical Imaging
Solid Dosage Forms
Multivariate Curve Resolution
Classical Least Squares
Genetic Algorithm
Espectroscopia de imagem
Infravermelho próximo
Algoritmos genéticos
Fármacos
Country: Reino Unido
Editor: Sage
Citation: Journal Of Near Infrared Spectroscopy. N I R Publications, v. 24, p. 157 - 169, 2016.
Rights: fechado
Identifier DOI: 10.1255/jnirs.1201
Address: https://journals.sagepub.com/doi/abs/10.1255/jnirs.1201
Date Issue: 2016
Appears in Collections:IQ - Artigos e Outros Documentos

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