Journal of Innovative Optical Health Sciences, 2017, 10 (3): 1650054, Published Online: Dec. 27, 2018
Comparative study on identification of healthy and osteoarthritic articular cartilages by fourier transform infrared imaging and chemometrics methods
Articular cartilage osteoarthritis Fourier transform infrared imaging partial least squares discriminant analysis Fisher's discriminant analysis
Abstract
Two discriminant methods, partial least squares-discriminant analysis (PLS-DA) and Fisher's discriminant analysis (FDA), were combined with Fourier transform infrared imaging (FTIRI) to differentiate healthy and osteoarthritic articular cartilage in a canine model. Osteoarthritic cartilage had been developed for up to two years after the anterior cruciate ligament (ACL) transection in one knee. Cartilage specimens were sectioned into 10 μm thickness for FTIRI. A PLS-DA model was developed after spectral pre-processing. All IR spectra extracted from FTIR images were calculated by PLS-DA with the discriminant accuracy of 90%. Prior to FDA, principal component analysis (PCA) was performed to decompose the IR spectral matrix into informative principal component matrices. Based on the di?erent discriminant mechanism, the discriminant accuracy (96%) of PCA-FDA with high convenience was higher than that of PLSDA. No healthy cartilage sample was mis-assigned by these two methods. The above mentioned suggested that both integrated technologies of FTIRI-PLS-DA and, especially, FTIRI-PCA-FDA could become a promising tool for the discrimination of healthy and osteoarthritic cartilage specimen as well as the diagnosis of cartilage lesion at microscopic level. The results of the study would be helpful for better understanding the pathology of osteoarthritics.
Zhi-Hua Mao, Yue-Chao Wu, Xue-Xi Zhang, Hao Gao, Jian-Hua Yin. Comparative study on identification of healthy and osteoarthritic articular cartilages by fourier transform infrared imaging and chemometrics methods[J]. Journal of Innovative Optical Health Sciences, 2017, 10(3): 1650054.