Saliva has shown considerable promise as a diagnostic medium for point-of-care (POC) and over-the-counter (OTC) diagnostic devices due to the non-invasive nature of its collection. However, a significant limitation of saliva-based detection is undesirable interference in a sensor's readout caused by interfering components in saliva. In this study, we develop standardized sample treatment procedures to eliminate bubbles and interfering molecules while preserving the sample's target molecules such as spike (S) protein and glucose. We then test the compatibility of the pretreatment system with our previously designed SARS-CoV-2 and glucose diagnostic biosensing systems for detecting S protein and glucose in subject saliva. Ultimately, the effectiveness of each filter in enhancing biomarker sensitivity is assessed. The results show that a 20 mg nylon wool (NW) filter shows an 80% change in viscosity reduction with only a 6% reduction in protein content, making it an appropriate filter for the salivary S protein diagnostic system. Meanwhile, a 30 mg cotton wool (CW) filter is identified as the optimal choice for salivary glucose detection, achieving a 90% change in viscosity reduction and a 60.7% reduction in protein content with a minimal 4.3% reduction in glucose content. The NW pretreatment filtration significantly improves the limit of detection (LOD) for salivary S protein detection by five times (from 0.5 nM to 0.1 nM) and it reduces the relative standard deviation (RSD) two times compared to unfiltered saliva. Conversely, the CW filter used for salivary glucose detection demonstrated improved linearity with an R of 0.99 and a sensitivity of 36.6 μA/mM·cm, over twice as high as unfiltered saliva. This unique filtration process can be extended to any POC diagnostic system and optimized for any biomarker detection, making electrochemical POC diagnostics more viable in the current market.