Biomedical subjects

Slobodan Sasić

Publications and source records attributed to Slobodan Sasić.

8 recordsLinked to original sources

PubMed2006

Raman mapping of low-content API pharmaceutical formulations. I. Mapping of Alprazolam in Alprazolam/Xanax tablets.

PURPOSE: Raman chemical imaging of API was carried out to provide information on whether there is any structural difference between the commercial Xanax and Alprazolam tablets (the API content less than 1% w/w in both cases) in the batches with low and good recovery. MATERIALS AND METHODS: Raman mapping spectra were collected from the flattened surfaces of six tablets. The mapping spectra were analyzed by principal component analysis because the Raman signal of the API (Alprazolam) was not reliably detected from the raw spectra. The complexity of the obtained grey-scale score images was such that no information about the domain sizes of the API could be obtained and thus binarization was applied to simplify these images. RESULTS: It was found that reliable detection of the Raman signal of the API was only achieved after principal component analysis was employed with the mapping being facilitated by a surprising similarity between a high principal component loading and the spectrum of the API. The binarization was successful only if the outlying pixels in the score images are eliminated. SUMMARY: The final chemical images represent quantitative characterization of the domains of the API in the tablets in contrast to chemical images of tablets that have been reported so far in the literature which have usually been descriptive only. The abundance of Alprazolam in all six tablets of Xanax and Alprazolam, respectively, was very similar. The domain sizes were found to be below 75 mum in diameter for all the tablets analyzed.

Alprazolam↗

PubMed2006

Chemical images: technical approaches and issues.

Chemical mapping techniques using Raman microscopy are introduced, and using an example of a pharmaceutical tablet, the practical aspects of data collection and processing to produce a chemical image of the sample are detailed. Issues related to data processing, instrument standards, chemical image reportable errors, and the interpretation of chemical images are presented to encourage debate, develop solutions, and promote use in other challenging scientific applications. applications.

Calibration↗

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.

Algorithms↗

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

Two-dimensional (2D) correlation coefficient analyses of heavily overlapped near-infrared spectra.

Two-dimensional (2D) correlation coefficient analysis is employed to classify and characterize spectral variations among heavily overlapped near-infrared spectra of pellets and films of three kinds of polyethylene (PE), high-density (HD), low density (LD), and linear low-density (LLD) polyethylene, and five kinds of ivory signature seals. The sample-sample (SS) 2D correlation maps are used for classification while the wavenumber-wavenumber (WW) 2D correlation maps are used for determining spectral variation among the above materials. Both correlation maps are obtained by multiplying the original data with themselves. It is found that the NIR spectra of pellets and films of HD PE are clearly different from those of LD PE and LLD PE, while the NIR spectra of five kinds of ivory seals yield easily discernable squares in the SS correlation maps. The background variation is thought to be behind the differentiation of the PE samples because the WW correlation maps do not indicate appearance of new bands. The correlation results are compared with those of principal component analysis (PCA). This study is a novel application of 2D correlation coefficient analysis which reveals that a comprehensive description of demanding spectral systems is achievable by utterly simple mathematical means because 2D correlation maps are obtained via a single mathematical operation.

Animals↗

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.

Algorithms↗

PubMed2003

Applications of moving window two-dimensional correlation spectroscopy to analysis of phase transitions and spectra classification.

Our recently proposed idea of moving window two-dimensional (2D) correlation spectroscopy, which partitions a data set into series of relatively small submatrices (windows) and calculates their covariance maps in succession, is tested for three convoluted data set. Phase-transition temperatures of oleic acid and poly-(N-isopropylacrylamide) in an aqueous solution are sought by analyzing covariances of their temperature-dependent near-infrared and infrared spectra, respectively, while Raman spectra of three kinds of polyethylene (PE) pellets are investigated to find the spectral differences among them and to classify randomly ordered spectra by a sample-sample (SS) covariance map. The criterion of mean of standard deviation of covariance matrices is used as an indicator of the crucial information present in these matrices so that only a few of them are discussed in details. The results are obtained quickly after very simple calculations and are studied at length. The baseline variation is not removed prior to the calculations but is found to be of use for the determination of the phase-transition temperatures. Randomly ordered Raman spectra of the PE pellets are classified by innovatively used and interpreted SS slice spectra, with the relation to principal component analysis discussed.

Journal Article↗

PubMed2003

Moving window two-dimensional correlation spectroscopy and determination of signal-to-noise threshold in correlation spectra.

In this paper we report two new developments in two-dimensional (2D) correlation spectroscopy; one is the combination of the moving window concept with 2D spectroscopy to facilitate the analysis of complex data sets, and the other is the definition of the noise level in synchronous/asynchronous maps. A graphical criterion for the latter is also proposed. The combination of the moving window concept with correlation spectra allows one to split a large data matrix into smaller and simpler subsets and to analyze them instead of computing overall correlation. A three-component system that mimics a consecutive chemical reaction is used as a model for the illustration of the two ideas. Both types of correlation matrices, variable-variable and sample-sample, are analyzed, and a very good agreement between the two is met. The proposed innovations enable one to comprehend the complexity of the data to be analyzed by 2D spectroscopy and thus to avoid the risks of over-interpretation, liable to occur whenever improper caution about the number of co-existing species in the system is taken.

Artifacts↗