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Abstract Upon entering the environment, the surface properties of pristine plastics are rapidly altered due to interactions with exogenous biomolecules, contaminants, and even microbiota, which ultimately alter their ecological impacts. When present in biological fluids or high protein environments, micro(nano)plastics bind with proteins, which form a protein corona around the particle. Although a significant body of literature exists on protein corona formation on nanomaterials, less is known about how the physiochemical properties of microplastics may influence protein corona formation. This study utilises quantitative proteomics to quantify protein binding to pristine and leached microplastics. Pristine polyethylene (PE) beads (50 and 500 μm), polyamide (PA) fibres (100 μm), polyethylene terephthalate fibres (500 μm), and fragments (

Bottom-up, end-user based feed, and food analysis through smartphone quantification of lateral flow assays (LFA) has the potential to cause a paradigm shift in testing capabilities. However, most developed devices do not test the presence of and implications of inter-phone variation. Much discussion remains regarding optimum color space for smartphone colorimetric analyses and, an in-depth comparison of color space performance is missing. Moreover, a light-shielding box is often used to avoid variations caused by background illumination while the use of such a bulky add-on may be avoidable through image background correction. Here, quantification performance of individual channels of RGB, HSV, and LAB color space and ΔRGB was determined for color and color intensity variation using pH strips, filter paper with dropped nanoparticles, and colored solutions. LAB and HSV color space channels never outperformed the best RGB channels in any test. Background correction avoided measurement variation if no direct sunlight was used and functioned more efficiently outside a light-shielding box (prediction errors < 5%/35% for color/color intensity change). The system was validated using various phones for quantification of major allergens (i.e., gluten in buffer, bovine milk in goat milk and goat cheese), and, pH in soil extracts with commercial pH strips and LFA. Inter-phone variation was significant for LFA quantification but low using pH strips (prediction errors < 10% for all six phones compared). Thus, assays based on color change hold the strongest promise for end-user adapted smartphone diagnostics.