## 4 Discussion  
To the authors' knowledge, this is the first study evaluating the impact of long-term sample storage on the quality of MIR spectra of serum and synovial-fluid samples used for discriminating OA from control samples. Both serum and synovial fluid samples spectra had statistically significant differences in spectral variables due to the aging of the samples; therefore, our hypothesis was rejected. However, the contributions of these differences to the overall spectral variability for both serum and synovial fluid were relatively small (i.e., <3%). It is important to note that despite the statistically significant differences between the spectra of samples from short-term versus long-term storage, these differences were not a significant component of the observed differences between OA and control samples. This is reflected in the continuously high performance of the predictive models (>87% for sensitivity, specificity, and accuracy) for both serum and synovial fluid samples despite the time of measurements. Visual assessment of spectra from different time points for serum and synovial fluid samples (Figures 2 and 4) also shows how there is a high consistency in the variables identified as putative spectroscopic markers of OA between the two models,  
Figure 4: Mean spectra of synovial fluid samples from short-term (**A**) and long-term storage (**B**) with highlighted lines (pink) outlining wavenumbers responsible for the observed differences in the spectra. Figure 3: Double-cross-validated projections of the outer loop of synovial fluid 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 synovial fluid samples after short-term storage (**A**) and long-term storage (**B**). CV, coefficient variable; OA, osteoarthritis; Ctrl, control. thus confirming that even if prolonged storage time can result in small but significant spectral differences, these differences do not affect the reliability and the interpretation of classification models built on the data. This finding supports the use of biobanking of samples for \(\sim\)5 years if MIR spectral fingerprints are used for discriminating between OA and control samples using the described methodology. This difference between the levels of variability due to aging in the samples (i.e., 1.61% in serum versus 2.98% in synovial fluid) may be partly due to the types of molecules in the synovial fluid that may be more vulnerable to freezing and degradation over time. But more importantly, the sample storage protocols may have a role in the observed differences. Serum is cell-free, whereas the synovial fluid preparation in this study involved freezing the sample without any further processing (i.e., no centrifugation or separation of cellular components pre-freezing). Therefore, the presence of cellular components and their unique degradation patterns may have resulted in the greater variability between the synovial fluid spectra based on time in a frozen state. The influence of different sample collection, processing, and freezing methods on results of metabolomics studies using synovial fluids have developed optimized protocols to minimize loss of data during measurements [25; 26]. Therefore, if the dried-film FTIR spectroscopy technique used in this study is to be compared with metabolomics and proteomic techniques, the sample processing and storage techniques must be uniform to allow for direct comparisons. This study shows a small but acknowledgeable impact of long-term storage that needs to be considered when utilizing this methodology for prospective clinical trials where samples are collected over years, particularly for chronic and insidious disease processes such as OA.