Near infrared spectroscopic (NIRS) analysis of drug-loading rate and particle size of risperidone microspheres by improved chemometric model.

摘要: Abstract Microspheres have been developed as drug carriers in controlled delivery systems for years. In our present study, near infrared spectroscopy (NIRS) is applied to analyze the particle size and loading rate risperidone poly( d , l -lactide-co-glycolide) (PLGA) microspheres. Various batches of PLGA microspheres were designed prepared successfully. The drug-loading all samples determined by a laser diffraction analyzer high performance liquid chromatography (HPLC) system. Monte Carlo algorithm combined with partial least squares (MCPLS) method was identify outliers choose numbers calibration set. Furthermore, series preprocessing methods performed remove signal noise NIR spectra. Moving window PLS radical basis function neural network (RBFNN) employed establish model. Our data demonstrated that PLS-developed model only suitable analysis Comparatively, RBFNN-based predictive models possess better fitting quality, effect, stability both analysis. correlation coefficients set (Rc2) 0.935 0.880, respectively. optimum RBFNN confirmed independent verification test 15 samples. Collectively, successfully monitor during preparation.