Background: Accurate diagnosis of native joint septic arthritis (NJSA) and Inflammatory arthropathies for appropriate and timely management. This study presents preliminary findings from the SPECTRAL Biobank evaluating spectroscopy as a rapid, objective method for differentiating NJSA from Pseudogout and Gout. Method: 1-2mL of joint fluid aspirates (JFAs) were collected following microbiological assessment from Northern Health Microbiology Laboratory. Samples were stored and analysed at the Northern Centre for Health Education Research laboratory. Patient demographic/clinical data, including diagnostic results of the JFAs, were recorded. JFAs were analysed utilising Fourier Transform Infrared and Ramen Spectroscopy, whereby each sample generated a unique spectral signature (~60 seconds). Spectroscopy is a non-destructive, chemically label-free, reproducible, and repeatable chemical analysis technology. Prior to analysis, the clinical and/or microbiological diagnosis was recorded for each sample for comparative analysis. Machine Learning Models of the Spectral Signatures were established utilising a) Unsupervised Cluster Analysis (CA) b) Principal Component Analysis (PCA) and c) Partial Least Squares Discriminant Analysis (PLS-DA). Results: More than 225 synovial fluid samples were collected between July 2024 and November 2025, with the following relevant confirmed diagnoses: 30 pseudogout, 41 gout, and 19 NJSA. Post spectral scanning; Machine Learning Models (CA, PCA and PLSDA) showed distinct grouping and clear delineation of Gout, Pseudogout, and Infection compared to one another. Thus, spectroscopic analysis of JFA may be used for rapid diagnostic classification of NJSA and inflammatory arthropathies with a confidence level of 95%. Specificity of NJSA, Pseudogout, and Gout were 98%, 95%, and 100% respectively, while sensitivity was 80%, 88%, and 75%. Conclusion: Spectroscopy shows great promise as an adjunct diagnostic tool, providing rapid and reliable differentiation of joint arthropathies.