Single-use medical plastics generate a complex waste stream that is poorly addressed in current recycling systems. Despite their significant potential for recycling, these materials are often incinerated or landfilled due to their heterogeneous nature, contamination risks, and lack of standardised protocols. This study developed a multimodal workflow combining attenuated total reflectance Fourier-transform infrared spectroscopy (ATR-FTIR) and hyperspectral imaging (HSI) to assess the recyclability of polypropylene (PP) medical plastics. Medical kidney dishes (yellow, n = 27; opaque, n = 4; transparent, n = 23) collected from the Queensland Children's Hospital were analysed. ATR-FTIR revealed compositional differences both between colour types and within individual dishes when a grid-scanning method was applied. HSI in the short-wave infrared range (1100-1700 nm) distinguished between colour groups; however, variation within groups and within individual dishes reflected optical effects; scattering, pigmentation, and thickness, rather than compositional differences. While suitable for rapid classification of sample type HSI was less effective at capturing the chemical variance between the same post consumer product types compared to ATR-FTIR. Principal Component Analysis (PCA) of both datasets confirmed that FTIR resolved chemical variance while HSI functioned as a rapid optical triage tool. To screen for polyethylene (PE) contamination, a critical concern for PP recyclate quality, the CH3:CH2 ratio (2876/2920 cm⁻¹) was calculated
per spectrum, with the 720 cm⁻¹ CH₂ rocking band examined as a qualitative PE indicator. Group-level differences in band ratios were assessed using the Kruskal-Wallis test, and within-dish spatial consistency was evaluated using the coefficient of variation across the 25-point grid. These findings demonstrate that ATR-FTIR resolved chemical variation in post-consumer medical plastics and provided spatial information when combined with a grid-sampling approach. HSI provides discrimination of sample type but was less sensitive to small compositional differences within groups. Together, this workflow supports informed recycling decisions for medical plastic waste, contributing to circular economy initiatives in healthcare.