Data Analysis Raman Images

L. Zhang, M. Henson and S. Sekulic, Multivariate data analysis for Raman imaging of a model pharmaceutical tablet. Anal Chim. Acta, 545(2), 262-278 (2005). 

Colon tissue was selected as a model for the comparative analysis of soft tissue by FT-IR and Raman imaging at low and high lateral resolution, because it contains aU four major tissue types such as muscle, connective tissue, epithelium and also nerve cells. The vibrational spectroscopic fingerprints of normal tissues and their distribution in control samples were determined. The compilation of such data is important before a method can be applied to pathological colon tissue such as colorectal adenocarcinoma, which is the third most common form of cancer and the second leading cause of death among cancer patients in the Western world. Colorectal adenocarcinomas originate from epithelial cells and are able to infiltrate the subjacent layers of colon and rectum. 

Shinzawa, H., Awa, K, Kanematsu, W. Ozaki, Y. (2010). Multivariate Data Analysis for Raman Spectroscopic Imaging. Journal of Raman Spectroscopy, Vol. 40, No. 12, pp. 1720-1725... 

Process analyzer measurements, e.g., spectra or chemical images, typically require a mathematical transformation, e.g., multivariate data analysis, to correlate the process analytical data to a more relevant critical product attribute for design space definition. For brevity, throughout this section the measurement system that yields process analytical data is noted as a PAT method and the subsequent mathematical transformation is described as a model. To forego a debate regarding what constitutes a process analyzer, i.e., temperature sensor versus a Raman fiber optic probe, herein focuses on process analyzers that yield multivariate data. 

IR, Raman scrutinize homo/heteromolecular interaction (H-bonding, dipolar interaction), higher sensitivity of NIR for H-bonding (OH overtones/combinations) Amorphization generally weakens many inter-molecular interactions while strengthens some Overlapping peaks need deconvolution and multivariate data analysis for quantification, multi-component image analysis requires chemometric methods... 

JJ Andrew, TM Hancewicz. Rapid analysis of Raman image data using two-way multivariate curve resolution. Appl Spectrosc 52 797-807, 1998. 

E plots the Raman band and CARS signal at 1000 cm" that are assigned predominantly to proteins. A wider gap is similarly resolved here. Cluster analysis of the Raman image identifies three main groups of spectra that are displayed in Figure 3.5. The spectrum of the tissue (blue cluster) contains spectral contributions of proteins and nucleic acids, whereas lipid bands are weak. The intensities of lipid bands increase and the intensities of proteins and nucleic acids decrease in the spectra of the gaps. It can be concluded that the gap is filled with lipids of different density. The result of the underlying chemical composition can only be obtained by multivariate analysis of hyperspectral Raman data such as k-means cluster analysis. Similar observations were also made for brain tissue as described in the later section. 

Bonifacio, A. et al (2010) Chemical imaging of articular cartilage sections with Raman mapping, employing uni-and multi-variate methods for data analysis. Analyst, 135 (12), 3193—3204. 

Yerramshetty, J.S., Lind, C., and Akkus, O. (2006) The compositional and physicochemical homogeneity of male femoral cortex increases after the sixth decade. Bone, 39 (6), 1236-1243. Donnelly, E. et al. (2010) Effects of tissue age on bone tissue material composition and nanomechanical properties in the rat cortex. J. Biomed. Mater. Res. A, 92 (3), 1048-1056. Donnelly, E. et al (2010) Contribution of mineral to bone structural behavior and tissue mechanical properties. Calcif. Tissue Int., 87 (5), 450—460. Pathak, S. et al (2012) Assessment of lamellar level properties in mouse bone utilizing a novel spherical nanoindentation data analysis method. J. Mech. Behav. Biomed. Mater., 13, 102—117. Burket, J.C. et al (2013) Variations in nanomechanical properties and tissue composition within trabeculae from an ovine model of osteoporosis and treatment. Bone, 52 (1), 326-336. Carden, A. et al (2003) Ultrastructural changes accompanying the mechanical deformation of bone tissue a Raman imaging study. Calcif. Tissue Int., 72 (2), 166-175. 

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