Variable clustering and spectral angle mapper-orthogonal projection method for Raman mapping of compound detection in tablets (original) (raw)
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Data processing of vibrational chemical imaging for pharmaceutical applications
Journal of Pharmaceutical and Biomedical Analysis, 2014
Vibrational spectroscopy (MIR, NIR and Raman) based hyperspectral imaging is one of the most powerful tools to analyze pharmaceutical preparation. Indeed, it combines the advantages of vibrational spectroscopy to imaging techniques and allows therefore the visualization of distribution of compounds or crystallization processes. However, these techniques provide a huge amount of data that must be processed to extract the relevant information.
Multivariate data analysis for Raman imaging of a model pharmaceutical tablet
Analytica Chimica Acta, 2005
Spectroscopic imaging techniques provide spatial and spectral information about a sample simultaneously and are finding ever-increasing application in the pharmaceutical industry. Effective extraction of chemical information from imaging data sets is a crucial step during the application of imaging techniques. Multivariate imaging data analysis methods have been reported but few applications of these methods for pharmaceutical samples have been demonstrated. In this study, a bilayer model tablet consisting of avicel, lactose, sodium benzoate, magnesium stearate and red dye was prepared using custom press tooling, and Raman mapping data were collected from a 400 m × 400 m area of the tablet surface. Several representative multivariate methods were selected and used in the analysis of the data. Multivariate data analysis methods investigated include principal component analysis (PCA), cluster analysis, direct classical least squares (DCLS) and multivariate curve resolution (MCR). The relative merits and drawbacks of each technique for this application were evaluated. In addition, some practical issues associated with the use of these methods were addressed including data preprocessing, determination of the optimal number of clusters in cluster analysis and the optimization of window size in second derivative calculation.
Pharmaceutical applications of vibrational chemical imaging and chemometrics: A review
Journal of Pharmaceutical and Biomedical Analysis, 2008
The emergence of chemical imaging (CI) has gifted spectroscopy an additional dimension. Chemical imaging systems complement chemical identification by acquiring spatially located spectra that enable visualization of chemical compound distributions. Such techniques are highly relevant to pharmaceutics in that the distribution of excipients and active pharmaceutical ingredient informs not only a product's behavior during manufacture but also its physical attributes (dissolution properties, stability, etc.). The rapid image acquisition made possible by the emergence of focal plane array detectors, combined with publication of the Food and Drug Administration guidelines for process analytical technology in 2001, has heightened interest in the pharmaceutical applications of CI, notably as a tool for enhancing drug quality and understanding process. Papers on the pharmaceutical applications of CI have been appearing in steadily increasing numbers since 2000. The aim of the present paper is to give an overview of infrared, near-infrared and Raman imaging in pharmaceutics. Sections 2 and 3 deal with the theory, device set-ups, mode of acquisition and processing techniques used to extract information of interest. Section 4 addresses the pharmaceutical applications.
Journal of Pharmaceutical and Biomedical Analysis, 2014
In this work, Raman hyperspectral images and Multivariate Curve Resolution Alternating Least 14 Squares (MCR-ALS) are used to study the distribution of actives and excipients within a 15 pharmaceutical drug product. This article is mainly focused on the distribution of a low dose 16 constituent. Different approaches are compared, using initially filtered or non-filtered data, or using 17 a column-wise augmented dataset before starting the MCR-ALS iterative process including appended 18 information on the low dose component. In the studied formulation, magnesium stearate is used as a 19 lubricant to improve powder flowability. With a theoretical concentration of 0.5% w/w in the drug 20 product, the spectral variance contained in the data is weak. By using a Principal Component Analysis 21 (PCA) filtered dataset as a first step of the MCR-ALS approach, the lubricant information is lost in the 22 non-explained variance and its associated distribution in the tablet cannot be highlighted. A sufficient 23 number of components to generate the PCA noise-filtered matrix has to be used in order to keep the 24 lubricant variability within the data set analyzed or, otherwise, work with the raw non-filtered data. 25
Nature protocols, 2015
Raman and Fourier transform IR (FTIR) microspectroscopic images of biological material (tissue sections) contain detailed information about their chemical composition. The challenge lies in identifying changes in chemical composition, as well as locating and assigning these changes to different conditions (pathology, anatomy, environmental or genetic factors). Multivariate data analysis techniques are ideal for decrypting such information from the data. This protocol provides a user-friendly pipeline and graphical user interface (GUI) for data pre-processing and unmixing of pixel spectra into their contributing pure components by multivariate curve resolution-alternating least squares (MCR-ALS) analysis. The analysis considers the full spectral profile in order to identify the chemical compounds and to visualize their distribution across the sample to categorize chemically distinct areas. Results are rapidly achieved (usually <30-60 min per image), and they are easy to interpret ...
Multivariate data analysis for Raman spectroscopic imaging
Journal of Raman Spectroscopy, 2000
This article reviews the analytic techniques for Raman spectroscopic imaging with emphasis on chemometrics. Key information included in Raman spectra is often distributed broadly throughout the dataset. It is possible to condense the information into a very compact matrix representation by a chemometric technique of factor analysis such as principal component analysis (PCA) or self-modeling curve resolution (SMCR). PCA yields two matrices called scores and loadings which complementarily represent the entire features broadly distributed in the dataset. This concept can be further extended to other forms of data transformation schemes, including bilinear data decomposition based on SMCR analysis. SMCR offers a firmer model which is chemically or physically interpretable. The information derived from these techniques readily brings useful insight into building a mechanistic model for understanding complex phenomena studied by Raman spectroscopy. Illustrative examples are given for applications of both PCA and SMCR to Raman imaging of pharmaceutical tablets.
Raman Mapping of Spectrally Non-Well-Behaved Species Molecular Spectroscopy Workbench
2016
The use of Raman spectroscopy to produce material images whose contrast is derived from chemical or crystallographic species has been quite useful since the introduction of the Raman microscope in 1976, but particularly with the more recent development of more-sensitive and easier-to-use instruments. When the various species in the field of view have spectra with nonoverlapping analytical bands, simple univariate analysis can provide good images. When overlapping bands are present, multivariate techniques, especially multivariate curve resolution (MCR), have been successfully applied. However, there are cases where even MCR results may be problematic. In this installment, we look at some maps of a ceramic composite containing silicon carbide, silicon, boron carbide, and carbon, where each of these species has nonunique spectra to see what type of results flexible software can produce. What is the goal in this type of exercise? For some of us, creating images is like a teenager’s com...
European Journal of Pharmaceutical Sciences, 2009
Near Infrared Chemical Imaging (NIR-CI) is an attractive technique in pharmaceutical development and manufacturing, where new and more robust methods for assessment of the quality of the final dosage products are continuously demanded. The pharmaceutical manufacturing process of tablets is usually composed by several unit operations such as blending, granulation, compression, etc. Having reliable, robust and timely information about the development of the process is mandatory to assure the quality of the final product. One of the main advantages of NIR-CI is the capability of recording a great amount of spectral information in short time. To extract the relevant information from NIR-CI images, several Chemometric methods, like Partial Least Squares Regression, have been demonstrated to be powerful tools. Nevertheless, these methods require a calibration phase. Developing new methods that do not need any prior calibration would be a welcome development. In this context, we study the potential usefulness of Classical Least Squares and Multivariate Curve Resolution models to provide quantitative and spatial information of all the ingredients in a complex tablet matrix composed of five components without the development of any previous calibration model. The distribution of the analytes in the surfaces, as well as the quantitative determination of the five components is studied and tested.
Vibrational Spectroscopy Fingerprinting in Medicine: from Molecular to Clinical Practice
Materials
In the last two decades, Fourier Transform Infrared (FTIR) and Raman spectroscopies turn out to be valuable tools, capable of providing fingerprint-type information on the composition and structural conformation of specific molecular species. Vibrational spectroscopy’s multiple features, namely highly sensitive to changes at the molecular level, noninvasive, nondestructive, reagent-free, and waste-free analysis, illustrate the potential in biomedical field. In light of this, the current work features recent data and major trends in spectroscopic analyses going from in vivo measurements up to ex vivo extracted and processed materials. The ability to offer insights into the structural variations underpinning pathogenesis of diseases could provide a platform for disease diagnosis and therapy effectiveness evaluation as a future standard clinical tool.