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 [Formula: see text]m 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 different discriminant mechanism, the discriminant accuracy (96%) of PCA-FDA with high convenience was higher than that of PLS-DA. 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.
Concentration profiles of collagen and proteoglycan in articular cartilage by Fourier transform infrared imaging and principal component regression
