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.

Quantification of colorimetric assays with smartphones is being increasingly reported. However, a complete characterization of the performance of existing color spaces and single-color channels for optimum color/intensity change quantification is absent. Moreover, it has not been ascertained if it is necessary to utilize existing color spaces to reach optimal assay quantification. In this study, a randomized channel approach was adapted utilizing all single channels from RGB, HSV, and CieLab color space and all nonrepeating random combinations of two and three channels of these color spaces. Assays based on color or intensity change using pH strips and gold or carbon black nanoparticle-containing paper strips were optimized using this approach. Several novel channel combinations showed great promise, in terms of prediction error and interphone variation reduction, outperforming RGB, HSV, and CieLab color spaces. These novel combinations were used in a custom-developed smartphone application that performed automated background subtraction and polynomial regression for the quantification of a lateral flow assay for the detection of goat milk adulteration with cow milk and for pH prediction in soil. For the lateral flow assay the channel combination BSA was found optimum (mean average error = 36% ± 6%; R 2 = 0.97). For the soil pH assay the channel combination RLC was found optimum (mean average error = 1.31% ± 0.02%; R 2 = 0.997). The study has shown that nonclassical channel combinations for colorimetric quantification of specific assays are very promising and should be considered for smartphone-based analysis.