Smartphone-based lateral flow imaging system for detection of food-borne bacteria E.coli O157:H7.

We report an application for the smartphone as an accurate and unbiased reading platform of a lateral flow immunoassays for food safety application. In particular, this report focuses on detection of food-borne bacteria in samples extracted from food matrices such as ground beef and spinach. The lateral flow assay is a widely accepted methodology owing to its on-site results, low-cost analysis, and ease of use with minimum user inputs, even though sensitivity is not quite equivalent to that of standard laboratory equipment. An antibody-antigen relationship is transduced into a color change on a nitrocellulose pad while visual interpretation of this color change can result in uncertainty, particularly near the detection limit of the assay. Employing the high resolution integrated camera, constant illumination from light source, and computing power of a smartphone, we provide an objective and accurate method to determine the bacterial cell concentration in a food matrix based on the regression model from the color intensity of test lines. A 3D-printed sample holder was designed for representative commercial lateral flow assays and an in-house application was developed in Android Studio to solve the inverse problem to provide cell concentration information from the color intensity. Test results with E.coli O157:H7 as a model organism suggests that smartphone-based reader can detect 104-105 CFU/ml from ground beef and spinach food matrices.