We report an application of the smartphone as an accurate and unbiased reading platform of lateral flow assay. In particular, this report focuses on detection of food-borne bacteria from samples extracted from various food matrices. Lateral flow assay is widely accepted methodology due to its on-site result and low-cost analysis even though sensitivity is not as good as standard laboratory equipment. Antibody-antigen relationship is translated into a color change on the nitrocellulose pad and interpretation of this color change causes obscurity, particularly around the detection limit of the assay. Based on its integrated camera and computing power, we provide an objective and accurate method to determine the bacterial cell concentration from the food matrix based on the regression model based on the bacterial concentration and RGB channel color changes. 3-D printed sample holder was designed for one of the representative commercial lateral flow assay and in-house application was developed in Android studio that solves the inverse problem instantly to provide cell concentration to the user.
Based on its integrated camera, new optical attachment, and inherent computing power, we propose an instrument design and validation that can potentially provide an objective and accurate method to determine surface meat color change and myoglobin redox forms using a smartphone-based spectrometer. System is designed to be used as a reflection spectrometer which mimics the conventional spectrometry commonly used for meat color assessment. We utilize a 3D printing technique to make an optical cradle which holds all of the optical components for light collection, collimation, dispersion, and a suitable chamber. A light, which reflects a sample, enters a pinhole and is subsequently collimated by a convex lens. A diffraction grating spreads the wavelength over the camera’s pixels to display a high resolution of spectrum. Pixel values in the smartphone image are translated to calibrate the wavelength values through three laser pointers which have different wavelength; 405, 532, 650 nm. Using an in-house app, the camera images are converted into a spectrum in the visible wavelength range based on the exterior light source. A controlled experiment simulating the refrigeration and shelving of the meat has been conducted and the results showed the capability to accurately measure the color change in quantitative and spectroscopic manner. We expect that this technology can be adapted to any smartphone and used to conduct a field-deployable color spectrum assay as a more practical application tool for various food sectors.