The strongly overlapping infrared absorption features of atherosclerotic and normal rabbit aorta samples as governed by their water, lipid, and protein content render the direct evaluation of molecular characteristics obtained from infrared (IR) spectroscopic measurements challenging for classification. We have successfully applied multivariate data analysis and classification techniques based on partial least squares regression (PLS), linear discriminant analysis (LDA), and principal component regression (PCR) to IR spectroscopic data obtained by using a recently developed infrared attenuated total reflectance (IR-ATR) catheter prototype for future in vivo diagnostic applications. Training data were collected ex vivo from atherosclerotic and normal rabbit aorta samples. The successful classification results on atherosclerotic and normal aorta samples utilizing the developed data evaluation routines reveals the potential of spectroscopy combined with multivariate classification strategies for the identification of normal and atherosclerotic aorta tissue for in vitro and, in the future, in vivo applications.

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