Metodologia baseada em NIRS e Quimiometria para a determinação de parâmetros de qualidade da quitosana para fins biomédicos

Abstract

Chitosan is a biomaterial in which the main quality characteristics are molar mass (MM) and degree of deacetylation (DD), that influence almost all of its functional properties. Thus, it is essential to determine both, to the supply of a quality raw material. The standard methodologies used to determine these parameters are viscosimetry and medium infrared spectroscopy, which, although accurate, present some operational difficulties. A feasible alternative to overcome these problems is the development of methodologies based on the near infrared (NIR) spectroscopy and chemometrics, proposed in this work. To develop the multivariate model, it was necessary to increase the variation of the parameters of interest. Thus, ten batches of chitosan were produced varying the deacetylation times in 3, 4, 5, 6 and 7 hours, producing 5 samples per batch and 50 samples in total. All samples were characterized in terms of DD and MM, according to the reference methodologies. The same samples were also analyzed by NIR spectroscopy. The NIR spectra of the samples were recorded in triplicates in the spectral range of 9,000 to 4,000 cm , using 32 scans and 8 cm¹ resolution, totalizing 150 spectra. To build the models, several spectral pre-processings were evaluated in relation to the predictive capacity. The calibration (100 samples) and prediction (50 samples) sets were selected with the assistance of the SPXY algorithm. The predictive capacity of the built models using the full spectral range of work was also evaluated and compared with those built using the variables selected by algorithms of variable selection. The evaluation of the predictive capacity of the models was performed by the analysis of figures of merit. Based on these parameters, it was verified that the best spectral pre-processing was the 1st derivative with window of 5 and 1st order polynomial for the DD. For the MM, the best predictive performance was shown by EMSC. In general, it was verified that the models built using the regression coefficients generated by the Martens' Uncertainty Test (Jack-knife coefficients), presented better predictive performance than the models built with all the spectral variables or with the spectral variables selected by the Successive Projections Algorithm (SPA). The prediction errors obtained for DD and MM were 1.85% and 29.08 KDa, respectively. The error obtained for DD is smaller than the allowed for the reference method. However, for the molar mass, the model did not show satisfactory performance. Therefore, it is clear the viability of the applied methodologies based on NIR spectroscopy and chemometrics to determine the DD in chitosan for biomedical purposes, produced by CERTBIO.