Raman spectroscopic study and identification of multi-period osteoarthritis of canine knee joint

A home-made small-sized Raman spectrometer combined with machine learning algorithms was used to study and identify healthy and multi-period osteoarthritis (OA) canine knee joints. Nine canines were equally divided into three groups according to the post-operative (OA modeling) time of 2-month, 3-month and 7-month. Other two normal canines were used as control. It was found that the degeneration degree of cartilage was positively correlated with post-operative time by doing anatomical analysis. The mixed Raman spectra of cartilage and subchondral bone were collected and analyzed, which reveals subchondral bone demineralization and carbonate ion substituting into the apatite mineral during OA. Raman spectra combined with principal component analysis (PCA) further disclosed that collagen matrix became unordered, both content ratios of amide I/matrix and phenylalanine/matrix in OA cartilage and subchondral bone increased. Based on the PCA getting five principal components, all groups were effectively discriminated by Fisher discriminant analysis (FDA) with high accuracy of 91.07% for the validation set, as well as 95.45% for the test set. It suggests that Raman spectroscopy combined with machine learning is capable to become an effective tool to achieve in situ identification of multi-period OA with high accuracy and preclinical significance.