## 3 Results
The predictive models for discriminating serum OA samples (n = 50) from controls (n = 49) built on the spectra from short-term storage showed sensitivity, specificity, and accuracy of 87.3 \(\pm\) 3.7%, 88.9 \(\pm\) 2.4%, and 88.1 \(\pm\) 2.3%, respectively. The predictive models based on the same serum samples after long-term storage showed sensitivity, specificity, and accuracy of 92.5 \(\pm\) 2.6%, 97.1 \(\pm\) 1.7%, and 94.8 \(\pm\) 1.4%, respectively. In both cases, the very good discriminant ability of the models are graphically visualized in Figure 1, where the mean scores (and the corresponding confidence intervals) of the samples along the single canonical variate (discriminant direction) of the models are displayed. In both cases, it can be observed how almost all of the OA samples have positive scores along the direction, whereas many of the controls have negative coordinates. Having verified that both models provided accurate discrimination between OA and control samples, inspection of the spectral variables mostly contributing to the observed differentiation was conducted by analyzing the values of the variable importance in projection (VIP) indices. The VIP indices summarize the contribution of each experimental variable to the PLS-DA model and are normalized so that a greater-than-one criterion can be used to assess their significance. Accordingly, the wavenumbers mostly responsible for  
Figure 1: Double-cross-validated projections of the outer loop serum spectral variables onto the only canonical variate of the classification model showing the difference in the values of the scores (bars indicate mean and whiskers the corresponding 95% confidence intervals) between OA and control serum samples after short-term storage (**A**) and long-term storage (**B**). CV, canonical variate; OA, osteoarthritis; Ctrl, control. the observed differences between OA and control serum samples at each time point are presented in Figure 2.  
The analysis was then repeated for the synovial-fluid samples based on the OA (n = 51) and control (n = 52) groups. The classification model based on short-term storage synovial fluid spectra had sensitivity, specificity, and accuracy of 97.3 \(\pm\) 1.6%, 89.4 \(\pm\) 2.6%, and 93.4 \(\pm\) 1.6%, respectively, when evaluated on the outer loop of the rDCV procedure. The predictive models based on the same synovial-fluid samples after long-term storage showed sensitivity, specificity, and accuracy of 95.7 \(\pm\) 2.1%, 95.7 \(\pm\) 0.8%, and 95.8 \(\pm\) 1.1%, respectively. Also, in this case, the very good discriminant ability of the models is graphically visualized in Figure 3, where the mean scores (and the corresponding confidence intervals) of the samples along the single canonical variate (discriminant direction) of the models are displayed. On the other hand, the wavenumbers responsible for the observed differences between OA and control SF samples at each time point, identified by inspection of the VIP indices calculated from the corresponding PLS-DA models, are presented in Figure 4.  
Figure 3: _Cont._  
Figure 2: Mean spectra of serum samples from short-term (**A**) and long-term storage (**B**), with highlighted lines (pink) outlining wavenumbers responsible for the observed differences in the spectra.