The latter outperformed RGB and grayscale channels in sensitivity, repeatability, and precision, while ratiometric ambient light correction resulted in excellent robustness for light-intensity variation. For the smartphone assay, 8-bit RGB and grayscale colorimetric channels were compared with 16-bit raw intensity values. The system enables swift and objective screening and provides a control measure to verify LFA assay reliability. The pipeline features smartphone-driven LFA quantification and an liquid chromatography-mass spectrometry (LC-MS) method enabling direct quantification of the allergens contained in the LFA. Here, we present an alternative pipeline for secure on-site compliance testing, using allergens as a case study. Screening assays, for example, lateral flow assays (LFAs), can improve traceability but often lack the required reliability to guarantee compliance. The current food safety testing system, based on laboratory-based quantification, is difficult to scale up in line with the growth in the export market and does not enable traceability through the nodes of the food supply system. Thus, assays based on color change hold the strongest promise for end-user adapted smartphone diagnostics. Inter-phone variation was significant for LFA quantification but low using pH strips (prediction errors < 10% for all six phones compared). 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. 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). LAB and HSV color space channels never outperformed the best RGB channels in any test. 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. 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. Much discussion remains regarding optimum color space for smartphone colorimetric analyses and, an in-depth comparison of color space performance is missing. However, most developed devices do not test the presence of and implications of inter-phone variation. 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.
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