The Efficiency of Color Space Channels to Quantify Color and Color Intensity Change in Liquids, pH Strips, and Lateral Flow Assays with Smartphones

Abstract

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.

Smartphone-based optical assays in the food safety field

“…This method was successfully applied in Ref. [ 25 ] for the quantification of gold nanoparticle solutions with an LOD ~ two to three fold lower as with a benchtop spectrometer while R 2 values were >0.995.…”

A Microfluidic Paper-Based Analytical Device for Type-II Pyrethroid Targets in an Environmental Water Sample

“…R is the red intensity of the sample, is the red intensity of the blank, G is the green intensity of the sample, is the green intensity of the blank, B is the blue intensity of the sample, is the blue intensity of the blank. ΔRGB is advantageous as there is evidence of less variability in measurements between smartphone types and less chance that a small error in a single RGB channel would impact results [ 58 ]. By plotting the absolute color intensities with the increasing concentrations of the pyrethroids, a positive slope of the calibration curves was obtained and facilitated its application in quantitative analysis.…”

Six decades of lateral flow immunoassay: from determining metabolic markers to diagnosing COVID-19

“…Then the conjugated antibodies bind to the target analyte and migrate to the recognition zone. This part of the strip contains biological components that react with the formed analyte–antibody complex, which is manifested as a colored line in the test zone, while the line in the control zone indicates the correct flow of the substrate [32] , [34] , [36] . The color intensity of the test line, which is proportional to the analyte content in the sample, is evaluated visually or using special equipment (reader) [44] .…”

“…Among other factors that affect test's sensitivity and specificity are chemicals present in biosubstrates that can bind to the system's components and mediate false positives. Sensitivity of a test system is limited by the constant of dissociation of the antibody–antigen conjugate and by colorimetric detection [36] , [39] . Therefore, the modern strategies implemented by manufacturers to overcome these limitations are aimed at improving the characteristics of labels used (fluorescent, paramagnetic), which cannot be detected visually but require special devices, i.e.…”

“…Therefore, the modern strategies implemented by manufacturers to overcome these limitations are aimed at improving the characteristics of labels used (fluorescent, paramagnetic), which cannot be detected visually but require special devices, i.e. readers, for quantitative analysis [21] , [29] , [36] . Furthermore, automated detection reduces time expenditures and improves interpretation of results [18] , [40] .…”