The objective of this study was to evaluate near-infrared (NIR) spectroscopy and imaging as approaches to assess anastrozole entrapment within PLGA microparticles. By varying the polymer concentration, three batches containing the same amount of the drug were prepared. The spectral features that allow NIR drug quantitation were evaluated and compared with a best fit line algorithm. Actual entrapment efficiencies (EEF) determined via a destructive method were used for construction of calibration models using partial least square regression (PLS) or the algorithm. On the other hand, a chemical imaging system based on array detector technology was used to rapidly collect high contrast NIR images of the formulated microparticles. A quantitative measure of anastrozole entrapped was determined by calculating the percentage standard deviation of the distribution of pixel intensities in the PLS score images and histograms. Concerning conventional NIR analysis, both methods were equivalent for the prediction of EEF over the range of polymer levels studied. Correlation coefficients of more than 0.992 were obtained for either the calibration or prediction of EEF by the two methods; 0.392% and 0.374% were the standard errors of calibration and prediction (SEC and SEP) obtained for the prediction of EEF using the fit line, respectively, whereas the prediction of the EEF by the partial least square regression showed a SEC of 0.195% and SEP of 0.179%. As a result, the spectral best fit algorithm method compared favourably to the multivariate PLS method, but was easier to develop. In contrast, NIR spectral imaging was capable of clearly differentiating the three batches, both qualitatively and quantitatively. The percentage standard deviation increased progressively by increasing the ratio of drug-to-polymer concentrations. In conclusion, both NIR approaches were capable of accurate assessment of drug entrapment within microparticles. In addition, the NIR spectral imaging system provides a rapid approach for acquiring spatial and spectral information on microparticles.