Polynomial regression of multiple sensing variables for high-performance smartphone colorimeter

Abstract

A robust and adaptive smartphone-based colorimetric sensing platform is reported. It utilizes multiple regression analysis to address nonlinear concurrent variations of multiple sensing variables. The instrument can perform colorimetric measurement with improved accuracy over a wide range where both color and intensity information of a colorimetric signal varies independently often simultaneously. The instrument utilizes the smartphone in-built flash LED (λ = 400–700 nm) to illuminate the test sample and the phone’s CMOS camera as a detector, collecting and digitizing the reflected light from that sample. 3D printing technology is used to fabricate a specially designed optical enclosure that performs as a diffuser, neutral density filter, and reflector to ensure constant and uniform illumination of the sensing platform. Thus, an ultra-low-cost (< 3 USD) portable smartphone-based colorimetric diagnostic system becomes feasible along with an easy-to-use customized android app adaptable for multi-analyte assays. The performance of the colorimetric measurement system is validated by: (a) monitoring the concentration of a laser dye, (b) measuring the pH of drinking water, and (c) quantifying the chlorine concentration of shrimp ponds.