Classification of atherosclerotic rabbit aorta samples by mid-infrared spectroscopy using multivariate data analysis

Area of Science:

• Biomedical Engineering
• Spectroscopy
• Medical Diagnostics

Background:

• Atherosclerosis significantly alters aorta chemical composition (water, lipid, protein).
• Direct infrared (IR) spectroscopic analysis is challenging due to overlapping absorption bands and complex tissue matrices.
• Accurate differentiation of normal and diseased aorta tissue is crucial for diagnosis.

Purpose of the Study:

• To develop and validate a method for distinguishing normal from atherosclerotic rabbit aorta tissue using IR spectroscopy.
• To overcome the limitations of direct spectroscopic evaluation through advanced data analysis.
• To assess the potential for future in-vivo diagnostic applications.

Main Methods:

• Infrared (IR) spectroscopy, including Attenuated Total Reflectance (ATR) and reflectance IR microscopy, was employed.
• Multivariate analysis techniques, specifically partial least squares regression (PLS) and linear discriminant analysis (LDA), were applied to IR spectral data.
• A training dataset from known normal and atherosclerotic rabbit aorta samples was used for model development and blind testing.

Main Results:

• High predictive accuracy was achieved in classifying normal and atherosclerotic aorta samples during blind testing.
• The combination of IR spectroscopy and multivariate classification effectively identified differences in tissue composition.
• The developed method demonstrated robust performance in distinguishing between the two tissue types.

Conclusions:

• IR spectroscopy coupled with multivariate classification strategies provides a powerful tool for in-vitro identification of normal and atherosclerotic aorta.
• This technique shows significant potential for non-invasive or minimally invasive diagnostic applications in cardiovascular disease.
• Future research could focus on refining the method for in-vivo measurements and clinical translation.