Keywords
Lateral Flow Assay (LFA), Point-of-Care (POC), paper-based, microfluidics, reflective sensor, low resource, quantificaiton
Presentation Type
Event
Research Abstract
Paper-based point-of-care (POC) diagnostics is a growing field in global health due to the extreme portability, accuracy, affordability, and ease of use of these tests. Advancements in recent years have led to more accurate detection and improved functionality using multistep molecular diagnostics. Many such assays utilize lateral flow detection strips for visualization of diagnostic results by eye, which limits the results to qualitative Yes/No readouts. This project focused on combining recent developments in paper-based POC diagnostics to develop and optimize an in-house built quantitative paper-based diagnostic reader for lateral flow detection in low-resource settings. Initially different sensors, including photocell sensors, reflective IR sensors, and light-to-frequency converters were tested to optimize detection method in terms of accuracy, precision, and affordability. After the detection method was determined, the sensor was calibrated using hCG (pregnancy) test strips from Wondfo® Co. to compare the in-house detection method to a standard image quantification through the ImageJ application. These analyses will be used to calibrate the circuit to relate the light intensity calculated to the concentration of analyte (hCG) present in the sample as well as determine the lower limit of detection (LLOD). The IR reflective sensor and the light-to-frequency converter performed best due to the wide detection output range, adaptability for dark environments, and consistency in detection. We will develop and calibrate both methods for future use. The detection methods determined in this project provide a platform for a multistep diagnostic device for DNA for uses in environmental, food, and health safety, and can be applied to other paper-based diagnostics for accurate quantified results.
Session Track
Flow Analysis and Processing
Recommended Citation
Megan Z. Chiu and Jacqueline Linnes,
"Quantification of Analyte Concentration from a Paper-Based Lateral Flow Assay Device Using Reflective Sensors"
(August 6, 2015).
The Summer Undergraduate Research Fellowship (SURF) Symposium.
Paper 123.
https://docs.lib.purdue.edu/surf/2015/presentations/123
Included in
Biomedical Devices and Instrumentation Commons, Diagnosis Commons, Public Health Commons
Quantification of Analyte Concentration from a Paper-Based Lateral Flow Assay Device Using Reflective Sensors
Paper-based point-of-care (POC) diagnostics is a growing field in global health due to the extreme portability, accuracy, affordability, and ease of use of these tests. Advancements in recent years have led to more accurate detection and improved functionality using multistep molecular diagnostics. Many such assays utilize lateral flow detection strips for visualization of diagnostic results by eye, which limits the results to qualitative Yes/No readouts. This project focused on combining recent developments in paper-based POC diagnostics to develop and optimize an in-house built quantitative paper-based diagnostic reader for lateral flow detection in low-resource settings. Initially different sensors, including photocell sensors, reflective IR sensors, and light-to-frequency converters were tested to optimize detection method in terms of accuracy, precision, and affordability. After the detection method was determined, the sensor was calibrated using hCG (pregnancy) test strips from Wondfo® Co. to compare the in-house detection method to a standard image quantification through the ImageJ application. These analyses will be used to calibrate the circuit to relate the light intensity calculated to the concentration of analyte (hCG) present in the sample as well as determine the lower limit of detection (LLOD). The IR reflective sensor and the light-to-frequency converter performed best due to the wide detection output range, adaptability for dark environments, and consistency in detection. We will develop and calibrate both methods for future use. The detection methods determined in this project provide a platform for a multistep diagnostic device for DNA for uses in environmental, food, and health safety, and can be applied to other paper-based diagnostics for accurate quantified results.