Wang Liqun, Chapman Jessica, Palmer Richard A, Alter Todd M, Hooper Brett A, van Ramm Olaf, Mizaikoff Boris
School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332-0400, USA.
Appl Spectrosc. 2006 Oct;60(10):1121-6. doi: 10.1366/000370206778664608.
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.
动脉粥样硬化兔主动脉样本和正常兔主动脉样本因其水、脂质和蛋白质含量而具有强烈重叠的红外吸收特征，这使得从红外（IR）光谱测量中获得的分子特征的直接评估对于分类具有挑战性。我们已经成功地将基于偏最小二乘回归（PLS）、线性判别分析（LDA）和主成分回归（PCR）的多变量数据分析和分类技术应用于通过使用最近开发的用于未来体内诊断应用的红外衰减全反射（IR-ATR）导管原型获得的红外光谱数据。训练数据是从动脉粥样硬化兔主动脉样本和正常兔主动脉样本离体收集的。利用开发的数据评估程序对动脉粥样硬化和正常主动脉样本的成功分类结果揭示了光谱学与多变量分类策略相结合在体外以及未来在体内应用中识别正常和动脉粥样硬化主动脉组织的潜力。
