Characterisation of Cartilage Damage via Fusing Mid-Infrared, Near-Infrared, and Raman Spectroscopic Data

Mid-infrared spectroscopy (MIR), near-infrared (NIR), and Raman are all well-established analytical techniques in biomedical applications. Since they provide complementary chemical information, we aimed to determine whether combining them amplifies their strengths mitigates weaknesses. This study investigates the feasibility of fusion MIR, NIR, spectroscopic data for characterising articular cartilage integrity. Osteochondral specimens from bovine patellae were subjected mechanical enzymatic damage, then acquired damaged control specimens. We assessed capacity individual methods classify samples into damage or groups using Partial Least Squares Discriminant Analysis (PLS-DA). Multi-block PLS-DA was carried out assess potential by dataset applying two-block (MIR MIR Raman, NIR Raman) three-block approaches (MIR, Raman). The results one-block models show a higher classification accuracy (93%) (92%) than (76%) spectroscopy. In contrast, observed highest efficiency 94% 93% NIR) models, respectively. detailed correlative analysis spectral features contributing discrimination adds considerably more insight molecular origin damage.