Biomedical subjects

Donald A Clark

Publications and source records attributed to Donald A Clark.

3 recordsLinked to original sources

PubMed2006

Defining a strategy for chemical imaging of industrial pharmaceutical samples on Raman line-mapping and global illumination instruments.

The performance of line-mapping and global illumination Raman systems for two pharmaceutical tablets and a powder blend are assessed in this study. The chemical images were obtained from the placebo, real tablets, and powder blend by using x20, x50, and x100 objectives, as well as via the (pseudo) confocal set-up. The chemical images were produced via univariate wavenumbers and as re-folded principal component (PC) scores (known as score images). In most cases it was easy to image two or three major components of the tablets directly, while the minor components were only imaged via PC scores. The active pharmaceutical ingredients (APIs) were located relatively easily even if present in quite low concentrations (less than 1%) owing to the high Raman scattering coefficients of these materials. The strength of the Raman signal of the API makes it almost ubiquitous in the chemical images of real tablets. Thorough discussion is given on the strategies used to produce chemical images, the prospects of making composite images of all components present in the tablets, and the effects of packing density with relation to the diffusion of the excitation laser light inside the sample. The strengths and weaknesses of the Raman imaging techniques used are emphasized and suggestions are given regarding which instrument is preferable with respect to the goal of the experiment and material under study. For example, mapping technology is preferred for analyzing minor components, while the global illumination approach is recommended for imaging of spatially isolated strong Raman scatterers.

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PubMed2005

Raman line mapping as a fast method for analyzing pharmaceutical bead formulations.

This paper describes the use of principal component analysis (PCA) to de-noise Raman spectra and considerably shorten data acquisition time in Raman mapping experiments. A solid dosage pharmaceutical material (bead) is mapped by a Raman line-mapping system. The mapping acquisition time was varied from 30 s (usually employed in practice) to only 3 s. Apparently excessive noise in the maps measured for 3 s is removed by PCA and the maps of all three components of the bead are then binarized and compared. It is found that spatial difference is negligible despite the remarkably different acquisition times employed. The spectra acquired for 3 s and reconstructed via PCA are found to largely overlap with the spectra acquired for 30 s. The signal to noise ratio of the Raman mapping spectra does not obey the expected root t dependence, thereby preventing straightforward estimation of the shortest usable acquisition time (which is to a lesser extent also a function of the binarization threshold). The results reveal that Raman microscopy can be considered a fast method for mapping some materials, in contrast to the established opinion that it is an inherently slow technique.

Chemistry, Pharmaceutical↗

PubMed2005

Analyzing Raman maps of pharmaceutical products by sample-sample two-dimensional correlation.

Sample-sample (SS) two-dimensional (2D) correlation spectroscopy is applied in this study as a spectral selection tool to produce chemical images of real-world pharmaceutical samples consisting of two, three, and four components. The most unique spectra in a Raman mapping spectral matrix are found after analysis of the covariance matrix. (This is obtained by multiplying the original mapping data matrix by itself.) These spectra are identified by analyzing the slices of the covariance matrix at the positions where covariance values are at maxima. Chemical images are subsequently produced in a univariate fashion by visually selecting the wavenumbers in the extracted spectra that are least overlapped. The performance of SS 2D correlation is compared with principal component analysis in terms of highlighting the most prominent spectral differences across the whole data set (which typically comprises several thousand spectra) and determining the total number of species present. In addition, the selection of the unique spectra by SS 2D correlation is compared with the selection obtained by the orthogonal projection approach (OPA). Both comparisons are found to be satisfactory and demonstrate that a quite simple SS 2D correlation routine can be used for producing reliable images of unknown samples. The main benefit of using SS 2D correlation is that it is based on a few data processing commands that can be executed separately and produce results that are closely related to the chemical features of the system.

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