The objective of this study was to evaluate near-infrared (NIR) spectroscopic imaging as a tool to assess a pharmaceutical quality assurance problem--blend uniformity in the final dosage product. A system based on array detector technology was used to rapidly collect high-contrast NIR images of furosemide tablets. By varying the mixing, 5 grades of experimental tablets containing the same amount of furosemide and microcrystalline cellulose were produced, ranging from well blended to unblended. For comparison, these tablets were also analyzed by traditional NIR spectroscopy, and both approaches were used to evaluate drug product homogeneity. NIR spectral imaging was capable of clearly differentiating between each grade of blending, both qualitatively and quantitatively. The spatial distribution of the components was based on the variation or contrast in pixel intensity, which is due to the NIR spectral contribution to each pixel. The chemical nature of each pixel could be identified by the localized spectrum associated with each pixel. Both univariate and partial least squares (PLS) images were evaluated. In the suboptimal blends, the regions of heterogeneity were obvious by visual inspection of the images. A quantitative measure of blending was determined by calculating the standard deviation of the distribution of pixel intensities in the PLS score images. The percent standard deviation increased progressively from 11% to 240% from well blended to unblended tablets. The NIR spectral imaging system provides a rapid approach for acquiring spatial and spectral information on pharmaceuticals. The technique has potential for a variety of applications in product quality assurance and could affect the control of manufacturing processes.

A commercial dispersive Raman spectrometer operating at 785 nm with a CCD detector was used to acquire spectra of USP reference materials inside amber USP vials. The laser and collection beams were directed through the bottom of the vials, resulting in a 60% loss of signal. The Raman shift was calibrated with a 4-acetamidophenol standard, and spectral response was corrected with a luminescent standard. After these corrections, the Raman spectra obtained inside the USP vial and on open powders differed by less than 5%. A spectral library of 309 reference materials was constructed, with spectral acquisition times ranging from 1 to 60 s. Of these, 8% had significant fluorescent background but observable Raman features, while 3% showed only fluorescence. A blind test of 26 unknowns revealed the accuracy of the library search to be 88-96%, depending on search algorithm, and 100% if operator discretion was permitted. The tolerance of the library search to degraded signal-to-noise ratio, resolution, and Raman shift accuracy were tested, and the search was very robust. The results demonstrate that Raman spectroscopy provides a rapid, noninvasive technique for compound identification.

The objective of this study was to evaluate near-infrared (NIR) spectroscopic imaging as a tool to assess a pharmaceutical quality assurance problem -blend uniformity in the final dosage product. A system based on array detector technology was used to rapidly collect high-contrast NIR images of furosemide tablets. By varying the mixing, 5 grades of experimental tablets containing the same amount of furosemide and microcrystalline cellulose were produced, ranging from well blended to unblended. For comparison, these tablets were also analyzed by traditional NIR spectroscopy, and both approaches were used to evaluate drug product homogeneity. NIR spectral imaging was capable of clearly differentiating between each grade of blending, both qualitatively and quantitatively. The spatial distribution of the components was based on the variation or contrast in pixel intensity, which is due to the NIR spectral contribution to each pixel. The chemical nature of each pixel could be identified by the localized spectrum associated with each pixel. Both univariate and partial least squares (PLS) images were evaluated. In the suboptimal blends, the regions of heterogeneity were obvious by visual inspection of the images. A quantitative measure of blending was determined by calculating the standard deviation of the distribution of pixel intensities in the PLS score images. The percent standard deviation increased progressively from 11% to 240% from well blended to unblended tablets. The NIR spectral imaging system provides a rapid approach for acquiring spatial and spectral information on pharmaceuticals. The technique has potential for a variety of applications in product quality assurance and could affect the control of manufacturing processes.

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