We present a nonparametric method of analysis of Raman spectra of coronary artery tissue to classify atherosclerotic lesions. The method correlates the principal component scores of the Raman spectra with the tissue pathology. A data set composed of 97 samples of human coronary artery was used to develop the diagnostic algorithm, and a second data set composed of 68 samples was then used to test this algorithm prospectively. The results show that the algorithm can accurately classify coronary artery tissue into three classes: nonatherosclerotic, noncalcified plaque, and calcified plaque. The accuracy of this classification scheme is comparable to that previously achieved by means of a biochemical analysis of the Raman spectra using the same data.
Geurt Deinum, Daniel Rodriguez, Tjeerd J. Romer, Maryann Fitzmaurice, John R. Kramer, and Michael S. Feld, "Histological Classification of Raman Spectra of Human Coronary Artery Atherosclerosis Using Principal Component Analysis," Appl. Spectrosc. 53, 938-942 (1999)