@article{Balbach:2021:10.1039/d1ay01209f,
author = {Balbach, S and Jiang, N and Moreddu, R and Dong, X and Kurz, W and Wang, C and Dong, J and Yin, Y and Butt, H and Brischwein, M and Hayden, O and Jakobi, M and Tasoglu, S and Koch, A and Yetisen, AK},
doi = {10.1039/d1ay01209f},
journal = {Analytical Methods: advancing methods and applications},
pages = {4361--4369},
title = {Smartphone-based colorimetric detection system for portable health tracking},
url = {http://dx.doi.org/10.1039/d1ay01209f},
volume = {13},
year = {2021}
}

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TY  - JOUR
AB  - Colorimetric tests for at-home health monitoring became popular 50 years ago with the advent of the urinalysis test strips, due to their reduced costs, practicality, and ease of operation. However, developing digital systems that can interface these sensors in an efficient manner remains a challenge. Efforts have been put towards the development of portable optical readout systems, such as smartphones. However, their use in daily settings is still limited by their error-prone nature associated to optical noise from the ambient lighting, and their low sensitivity. Here, a smartphone application (Colourine) to readout colorimetric signals was developed on Android OS and tested on commercial urinalysis test strips for pH, proteins, and glucose detection. The novelty of this approach includes two features: a pre-calibration step where the user is asked to take a photo of the commercial reference chart, and a CIE-RGB-to-HSV color space transformation of the acquired data. These two elements allow the background noise given by environmental lighting to be minimized. The sensors were characterized in the ambient light range 100–400 lx, yielding a reliable output. Readouts were taken from urine strips in buffer solutions of pH (5.0–9.0 units), proteins (0–500 mg dL−1) and glucose (0–1000 mg dL−1), yielding a limit of detection (LOD) of 0.13 units (pH), 7.5 mg dL−1 (proteins) and 22 mg dL−1 (glucose), resulting in an average LOD decrease by about 2.8 fold compared to the visual method.
AU  - Balbach,S
AU  - Jiang,N
AU  - Moreddu,R
AU  - Dong,X
AU  - Kurz,W
AU  - Wang,C
AU  - Dong,J
AU  - Yin,Y
AU  - Butt,H
AU  - Brischwein,M
AU  - Hayden,O
AU  - Jakobi,M
AU  - Tasoglu,S
AU  - Koch,A
AU  - Yetisen,AK
DO  - 10.1039/d1ay01209f
EP  - 4369
PY  - 2021///
SN  - 1759-9660
SP  - 4361
TI  - Smartphone-based colorimetric detection system for portable health tracking
T2  - Analytical Methods: advancing methods and applications
UR  - http://dx.doi.org/10.1039/d1ay01209f
UR  - https://pubs.rsc.org/en/content/articlelanding/2021/AY/D1AY01209F
UR  - http://hdl.handle.net/10044/1/96387
VL  - 13
ER  - 

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