Slobodan Šašić

An In-Depth Analysis of Raman and Near-Infrared Chemical Images of Common Pharmaceutical Tablets

This study reports on the application of Raman and near-infrared (NIR) imaging techniques for determining the spatial distribution of all (five) components in a common type of pharmaceutical tablet manufactured in two different ways. Multivariate chemical images were produced as principal component (PC) scores, while univariate images were produced by using the most unique spectra selected by the orthogonal projection approach (OPA), a searching algorithm. Multivariate Raman images were obtained for all five components in both tablets, while only two or three components could be imaged with the NIR instrument. Very interesting PC results are reported that in effect cast doubt on the effectiveness of the established criteria for determining signal-related PCs in the Raman data. PCA has been found to be indispensable for imaging the minor components using the Raman data. Significant similarity between the multivariate and univariate chemical images has been noted despite there being considerable spectral overlap within the Raman and, especially, within the NIR mapping data sets. Gray-scale images are carefully thresholded, which allowed for quantitative comparison of the obtained binarized images. A thorough discussion is given on the problems and approximations needed for producing composite images.

New Insight into the Mathematical Background of Generalized Two-Dimensional Correlation Spectroscopy and the Influence of Mean Normalization Pretreatment on Two-Dimensional Correlation Spectra

Generalized two-dimensional (2D) correlation spectroscopy is considered from the point of view of linear algebra. It is shown that a synchronous spectrum is the same as the cross-product matrix of the experimental data. An asynchronous spectrum is also treated as the ordered scalar products of the dynamic vectors of the experimental matrix and its Hilbert transformation. This approach connects the theory of generalized 2D correlation spectroscopy with the well-known conceptions of classic correlation analysis. The importance of spectral normalization in the 2D correlation analysis and its influence on the 2D correlation spectra is also investigated. All calculations were done on a synthetic spectral model consisting of two components. The synchronous spectra obtained from the model matrix were compared with those obtained after spectral mean normalization. It was found that the results strongly depend on the pretreatment. We plotted the Hilbert transformation of the meancentered model and found that the normalization leads to the disappearance of the asynchronous spectra in the two-component system. Also, it has been concluded that the influence of normalization is important just for the systems with large intensity variations. All the results presented here are quite general and can be applied irrespective of the nature of the perturbation.

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 ×20, ×50, and ×100 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.

A comparison of Raman chemical images produced by univariate and multivariate data processing—a simulation with an example from pharmaceutical practice

A direct comparison of univariate and multivariate data analysis has been performed to show the effect of spectral noise on the quality of chemical images derived from hyper-spectral data cubes. A data processing approach has been developed using a numerical model, based on spectra of common pharmaceutical excipients, and then applied to a real multi-layered solid dosage formulation. The results of this study demonstrate that the multivariate analysis, which in its simplest form only de-noises data using principal component analysis (PCA), produces significantly better quality chemical images than the univariate approach, even from data sets which appear visually poor. If pure component spectra are available, ordinary least squares (OLS) regression offers even better results. The ability to de-noise spectra using these approaches impacts on Raman experimental conditions and increases information content collected per unit time. Data acquisition time, which is a rate limiting step in the production of chemical images using Raman mapping and imaging techniques, is reduced by 60% and still produces multivariate chemical images of appropriate quality with which to study pharmaceutical formulations.

Band Assignment of Near-Infrared Spectra of Milk by Use of Partial Least-Squares Regression

Band assignment of near-infrared (NIR) spectra of milk has been investigated by the partial least-squares (PLS) regression method in the spectral regions of 1150–1850 and 2030–2380 nm. The shorter wavelength region (1150–1850 nm) was divided into three parts (1150–1350, 1350–1650, and 1650–1850 nm), and each part was analyzed separately. The band assignment was made through the analysis of the shapes of loadings and spectral and concentration variances explained by them. Bands at 1722 and 1754 nm and that at 1208 nm are assigned to first and second overtones of CH2 stretching vibrations of milk fats, respectively. It is revealed from the PLS regression of milk samples with constant protein content and varying fat content that two kinds of water bands exist in the 1350–1650 nm region; the first group of water bands at 1400, 1440, and 1520 nm is dependent upon the fat content, while the second group of bands at 1406 and 1486 nm seems to depend on the protein content. For all three parts in the 1150–1850 nm region it is found that other milk components such as proteins and lactose do not interfere significantly with the fat bands. For the longer wavelength region (2030–2380 nm), bands at 2302 and 2340 nm are assigned to a combination of CH2 symmetric stretching and symmetric bending vibrations of fats. No interference from other bands with the fat bands appears, although this region is rather rich in protein bands. For the proteins, only weak bands are identified at 2056, 2160, 2316, 2340, and 2368 nm. The band at 2056 nm is assigned to a combination of amide A and amide I modes, while the band at 2160 nm is due to that of amide B and amide II modes. The bands at 2316, 2340, and 2368 nm arise from combinations of CH2 stretching and bending modes of protein side chain groups. Other protein bands are strongly masked by baseline slope.

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.

Chemical imaging of pharmaceutical granules by Raman global illumination and near-infrared mapping platforms

Raman global illumination and near-infrared (NIR) mapping instruments were used to chemically image pharmaceutical granules obtained by the wet granulation process in order to determine whether the API was mixed with the major excipient or granulates on its own. The granules were randomly distributed onto a microscope slide and an average area of about 3.5mmx3.5mm, covering 50-100 granules, was analyzed by both instruments. Light microscopy images of the separated granules were collected before the spectroscopic data acquisition. Both Raman and NIR signals of API and major excipient (mannitol) were easily detected by both techniques which allowed the chemical structure of the granules to be characterised. Most of the granules were found to contain both API and mannitol but pure mannitol and a few pure API granules were also identified. Raman global illumination was found to provide a comprehensive insight into chemical structure of the granules being able to more clearly determine the API in comparison with NIR mapping. Owing to the differences in shapes of the particles and reflection characteristics, visual microscopy and methods based on reflection can be potentially useful for analyzing this particular formulation.

How can we unravel complicated near infrared spectra?—Recent progress in spectral analysis methods for resolution enhancement and band assignments in the near infrared region

This review paper reports recent progress in spectral analysis methods for resolution enhancement and band assignments in the near infrared (NIR) region. Spectra in the NIR region are inherently rich with information on the physical and chemical properties of molecules. However, it is not always straightforward to analyse the spectra because an NIR spectrum consists of a number of overlapped bands due to overtones and combination modes. An NIR spectrum may be analysed by conventional spectral analysis methods, chemometrics or two-dimensional correlation spectroscopy. The following conventional methods are currently utilised to analyse NIR spectra: (a) derivatives, (b) difference spectroscopy, (c) Fourier self-deconvolution and (d) curve fitting. The derivative method is powerful in separating superimposed bands and correcting for a baseline slope. Conventional experimental methods for spectral analysis, such as isotope exchange and measurement of polarisation spectra, are also valid in the NIR region. Chemometrics is very useful for extracting information from NIR spectra. Among a variety of chemometrics methods, multiple linear regression, principal component analysis, principal component regression and partial least squares regression are most often used for qualitative and quantitative analysis. Recently, chemometrics has been used for resolution enhancement of NIR spectra. Particularly, loadings plots or regression coefficients are useful for separating overlapped bands and for making band assignments. Notable recent advances in the analysis of NIR spectroscopy are the development or introduction of new spectral analysis methods such as two-dimensional (2D) correlation spectroscopy and self-modelling curve resolution methods (SMCR). 2D correlation analysis enables enhancement of apparent spectral resolution by spreading spectral peaks over a second dimension. SMCR allows one to resolve the experimental matrix into concentration profiles and pure spectra of the involved species without prior knowledge of any of these features.

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.

Wavelength—Wavelength and Sample—Sample Two-Dimensional Correlation Analyses of Short-Wave Near-Infrared Spectra of Raw Milk

Short-wave near-infrared (NIR) spectra of raw milk have been analyzed in the 800–1100 nm region by two-dimensional (2D) correlation spectroscopy. In this study, we have used both the well-known generalized 2D correlation spectroscopy method, which yields correlation coefficients among spectral variances on all the wavelength points (wavelength-wavelength correlation), and a novel sample-sample correlation spectroscopy method, which gives correlation coefficients among the concentration dynamics of the species in the system. The sample-sample correlation spectroscopy develops correlation maps with the samples on the axes. First, a set of 34 spectra was ordered according to increasing fat content, while all other milk components varied freely. Both synchronous and asynchronous wavelength-wavelength correlation maps have shown strong baseline changes despite the use of multiplicative scatter correction as a pretreatment. Bands at 930 and 970 nm due to fat and water, respectively, have been found to be the most significant spectral features. The synchronous sample-sample correlation map was calculated from the original data matrix and was compared with the outer product of the fat concentration vector. The comparison has revealed that the main spectral variances in the raw milk spectra in the short-wave NIR region are due to fat. The same procedure was repeated for a set of 15 samples that contained a constant fat content and were ordered according to increasing protein content. Poor agreement was found between the outer product of the protein concentration vector and the synchronous sample-sample correlation map. This result suggests that the spectral variances in raw milk spectra that have a constant fat content are due not exclusively to proteins but also due to other milk components. Comparisons of partial least-squares (PLS) regression analysis of fat and proteins and both sample-sample and wavelength-wavelength 2D correlation analyses of raw milk spectra have been made.