Significance: Raman spectroscopy is a well-established analytical method in the fields of chemistry, industry, biology, pharmaceutics, and medicine. Previous studies have investigated optical imaging and Raman spectroscopy for osteoarthritis (OA) diagnosis in weight-bearing joints such as hip and knee joints. However, to realize early diagnosis or a curable treatment, it is still challenging to understand the correlations with intrinsic factors or patients' background.Aim: To elucidate the correlation between the Raman spectral features and pathological variations of human shoulder joint cartilage.Approach: Osteoarthritic cartilage specimens excised from the humeral heads of 14 patients who underwent shoulder arthroplasty were assessed by a confocal Raman microscope and histological staining. The Raman spectroscopic dataset of degenerative cartilage was further analyzed by principal component analysis and hierarchical cluster analysis.Results: Multivariate association of the Raman spectral data generated three major clusters. The first cluster of patients shows a relatively high Raman intensity of collagen. The second cluster displays relatively low Raman intensities of proteoglycans (PGs) and glycosaminoglycans (GAGs), whereas the third cluster shows relatively high Raman intensities of PGs and GAGs. The reduced PGs and GAGs are typical changes in OA cartilage, which have been confirmed by safranin-O staining. In contrast, the increased Raman intensities of collagen, PGs, and GAGs may reflect the instability of the cartilage matrix structure in OA patients. Conclusions:The results obtained confirm the correlation between the Raman spectral features and pathological variations of human shoulder joint cartilage. Unsupervised machine learning methods successfully yielded a clinically meaningful classification between the shoulder OA patients. This approach not only has potential to confirm severity of cartilage defects but also to determine the origin of an individual's OA by evaluating the cartilage quality.

Osteoarthritis is the most common disease in articular cartilage. Raman spectroscopy is a promising tool for early detection of degenerative changes in cartilage matrix. In this study, surgically resected humeral heads of 14 patients were subjected to pathological analysis using Raman spectroscopy with principal component analysis and hierarchical clustering analysis. In the result, Raman spectral data of each specimen were divided into three major cluster reflecting the alteration in molecular composition of cartilage matrix. We also found histological characteristics in the cluster, suggesting that Raman spectrum is a biomarker to determine the condition of the cartilage tissue.