Smartphone-based colorimetric detection system for portable health tracking
Smartphone-based colorimetric detection system for portable health tracking
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.
4361-4369
Balbach, Samira
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Jiang, Nan
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Moreddu, Rosalia
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Dong, Xingchen
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Kurz, Wolfgang
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Wang, Congyan
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Dong, Jie
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Yin, Yixia
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Butt, Haider
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Brischwein, Martin
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Hayden, Oliver
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Jakobi, Martin
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Tasoglu, Savas
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Koch, Alexander W.
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Yetisen, Ali K.
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14 October 2021
Balbach, Samira
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Jiang, Nan
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Moreddu, Rosalia
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Dong, Xingchen
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Kurz, Wolfgang
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Wang, Congyan
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Dong, Jie
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Yin, Yixia
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Butt, Haider
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Brischwein, Martin
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Hayden, Oliver
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Jakobi, Martin
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Tasoglu, Savas
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Koch, Alexander W.
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Yetisen, Ali K.
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Balbach, Samira, Jiang, Nan, Moreddu, Rosalia, Dong, Xingchen, Kurz, Wolfgang, Wang, Congyan, Dong, Jie, Yin, Yixia, Butt, Haider, Brischwein, Martin, Hayden, Oliver, Jakobi, Martin, Tasoglu, Savas, Koch, Alexander W. and Yetisen, Ali K.
(2021)
Smartphone-based colorimetric detection system for portable health tracking.
Analytical Methods, 13 (38), .
(doi:10.1039/d1ay01209f).
Abstract
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.
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Published date: 14 October 2021
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© The Royal Society of Chemistry.
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Local EPrints ID: 503359
URI: http://eprints.soton.ac.uk/id/eprint/503359
ISSN: 1759-9660
PURE UUID: fcea70c9-7e98-41c8-bf31-08b87c0e3e3e
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Date deposited: 29 Jul 2025 17:02
Last modified: 30 Jul 2025 02:14
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Contributors
Author:
Samira Balbach
Author:
Nan Jiang
Author:
Rosalia Moreddu
Author:
Xingchen Dong
Author:
Wolfgang Kurz
Author:
Congyan Wang
Author:
Jie Dong
Author:
Yixia Yin
Author:
Haider Butt
Author:
Martin Brischwein
Author:
Oliver Hayden
Author:
Martin Jakobi
Author:
Savas Tasoglu
Author:
Alexander W. Koch
Author:
Ali K. Yetisen
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