Lateral flow assays (LFAs; aka. Rapid Tests) are inexpensive paper-based devices for rapid and specific detection of analyte of interest (e.g., COVID virus) in fluidic samples. Areas of application of LFAs cover a broad spectrum, spanning from agriculture, to food/water safety, to point-of-care medical testing, and most recently, detection of COVID-19 infection. While these low-cost and rapid tests are specific to the target analyte, their sensitivity and limit of detection is far inferior to their laboratory based counterparts. In addition, rapid tests normally cannot quantify the concentration of target analyte and only provide qualitative/binary detection. We have developed a low-cost, end-user sensing platform that significantly improves the sensitivity of the rapid test. The developed platform is based on Arduino and utilizes a low-cost far infrared, single-element detector to offer sensitive and semi-quantitative results from commercially-available rapid tests. The developed sensing paradigm is based on radiometric detection of photothermal responses of rapid tests in the frequency domain when exposed to modulated laser excitation. As a proof of principle, we studied commercially-available rapid tests for detection of THC (the principal psychoactive constituent of cannabis) in oral fluid with different concentrations of control positive solutions and subsequently read them with the developed sensor. Results suggest that the developed end-user sensor is not only able to improve the detection limit of the rapid test by approximately an order of magnitude (from 25ng/ml to 5ng/ml), but also offer the ability to obtain semi-quantitative insight into concentration of THC in the fluidic samples (<5ng/mL, 5-10ng/mL, 10-25ng/mL, >25ng/mL vs <25ng/mL,>25ng/mL).
Previous Article in event Previous Article in session
Next Article in event
Visualisation and analysis of digital and analog temperature sensors in PV generator through IoT softwareNext Article in session
Arduino-Based Sensing Platform for Rapid, Low-Cost, and High Sensitivity Detection and Quantification of Analytes in Fluidic Samples
Published: 01 November 2022 by MDPI in 9th International Electronic Conference on Sensors and Applications session Student Session
Keywords: THC; Lateral flow immunoassay; Arduino; Point-of-need; Photo-Thermal Radiometry