Image Credit: maRRitch/Shutterstock.com
Smartphone-based colorimetry revolutionizes biomedical practice with artificial intelligence and brings about unprecedented paradigms with potential roles in biosensor miniaturization, acoustic imaging, and radio signaling.
Evolution of Standard Colorimetry
Colorimetry concerns the numerical description of color stimuli through objective light measurements to avoid perceptual discrepancies between observers. This considers the psychophysical correspondence between color sensation and light radiation, which involves the light source, the reflection or transmission of the light by an object, and the human eye.
Following a transition from qualitative to quantitative measurement of light, numerous scientific and industrial institutions have emerged over the last century to align and normalize dispersed approaches across the scientific disciplines of physics, physiology, and psychology.
Photometers were among the pioneering spectroscopic devices to make strength measurements for the full range of electromagnetic light radiation in gas and electric lighting industries. Modern spectrophotometers can also affordably measure object temperature and chemical content of sample materials with high accuracy resolution in the visible range.
Colorimeters rely on psychophysical analysis and are more economical than spectrophotometers, which provide greater versatility and performance at the slightly higher expense of additional signal processing for subsequent optional calculation of colorimetric data. Examples of this process are the determination of environmental air and water quality and the forensic analysis of biological fluids such as blood.
Current colorimetric technology transforms spectral measurements that do not need reiterative calibration in streamlined pass-fail routines to meet the requirements of different color standards or color-order systems for image quality control. This applies to highly regulated industries, including manufacturing, construction, transportation, and medical, which is discussed below.
Color management and the measurement of color defects are also crucial in a vast range of industrial sectors, for example, food, textile, paper, leather, graphic art reproduction, and display and imaging systems for cinematography, broadcasting, and high-tech products.
Colorimetry standards continue to evolve and are increasingly adopted for adequate product specification, inspection, and comparison of results across different measurement devices and organizations.
Smartphone-Based Colorimetry for Biomedical Applications
The prevailing miniaturization of optical and photoelectric sensors and the increasing processing power in the consumer market have enabled the technology of traditional bench-top laboratory instrumentation to merge and compete with that of lightweight, convenient smartphones. Colorimetric applications showing great potential include home monitoring, telemedicine for global healthcare, and wearable technology.
Peripheral equipment is increasingly adapted to in-built smartphone capability in biomedical and environmental practice to facilitate portable, innovative, and high-performance spectrometric colorimetry. Embedded systems for bioanalytical and diagnostic applications are underway mostly from Asian manufacturers to become more prevalent once regulatory guidelines become more established.
Colorimetric detection methods are widely used for safety analysis through spectrophotometers that detect the color change of chemical reactions to determine substance concentration. Novel systems automatically detect and interpret rapid diagnostic testing on smartphones with dedicated mobile applications to improve the diagnostic error uncertainty associated with this type of testing.
Technical shortcomings for error-free colorimetry measurements can include frequent recalibration for minor ambient light modifications, the assembly of sophisticated peripheral equipment, and the need for additional power to maintain the required light source conditions constant.
Sensor Characteristics and Performance
The performance of colorimetric biosensing is generally determined by the diverse range of smartphones' in-built light sources and sensors (for example, flashlights, cameras, and ambient light sensors). To this effect, charge-coupled devices (CCD) and complementary metal-oxide-semiconductor sensors (CMOS) are the leading technologies due to their relatively high sensitivity and pixel density.
High-quality cameras provide increasing resolution by filtering colors through separate filter arrays, restricting image transfer between CCD cells and leading to color masking. Cooled CCD cameras are used for low-light applications, which accelerates capture time and increases sensitivity due to lower thermal noise and higher signal-to-noise ratio.
Challenges for commercial viability include the improvement of spectral resolution with the further miniaturization of the CCD or CMOS sensors for the filter arrays, as well as pixel density for better image quality acquisition and data measurement.
Prospects for Colorimetry, Machine Learning, and Miniaturization
Miniaturized spectroscopic imaging builds tunable filters based on micro-electro-mechanical systems, metasurfaces, and nanomaterials. These are among the latest technologies likely to benefit considerably from the use of colorimetric techniques.
The optimization in sensor design can further these technologies and their software by using machine learning techniques. This involves colorimetric data to train popular classification learning algorithms, such as support vector machines and recurrent neural networks (RNN), for bioanalytical detection testing.
Novel acoustic imaging incorporates frequency-color mapping so that color-coded sound levels determine the resolution of three-dimensional acoustic photographs using an RNN. Forthcoming applications in allied imaging fields also include ubiquitous computing for massive networked data collection and clinical diagnostics, agricultural, mineralogy, volcanology, and astrophysics.
References and Further Reading
A. K. R. Choudhury. (2014, Accessed on 6 April 2021). 6 - Colour measurement instruments. Principles of Colour and Appearance Measurement. Available: https://www.sciencedirect.com/science/article/pii/B9780857092298500061
C. Oleari. (2015, Accessed on 6 April 2021). 1 - Generalities on colour and colorimetry. Standard Colorimetry: Definitions, Algorithms and Software. Available: https://app.dimensions.ai/details/publication/pub.1043923834
A. J. S. McGonigle, et al. (2018, Accessed on 6 April 2021). Smartphone spectrometers. Sensors 18(1), 223. Available: https://app.dimensions.ai/details/publication/pub.1100417670
S. Kanchi, et al. (2018, Accessed on 6 April 2021). Smartphone based bioanalytical and diagnosis applications: A review. Biosensors and Bioelectronics 102, 136-149. Available: https://www.researchgate.net/publication/320883207_Smartphone_based_bioanalytical_and_diagnosis_applications_A_review
Z. Yang, et al. (2021, Accessed on 6 April 2021). Miniaturization of optical spectrometers. Science 371(6528), eabe0722. Available: https://app.dimensions.ai/details/publication/pub.1134948211
V. Kılıç, et al. (2020, Accessed on 6 April 2021). From sophisticated analysis to colorimetric determination: smartphone spectrometers and colorimetry. Color Detection. Available: https://app.dimensions.ai/details/publication/pub.1112012143
C. Park, et al. (2021, Accessed on 6 April 2021). The design and evaluation of a mobile system for rapid diagnostic test interpretation. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 5(1), Article 29. Available: https://drive.google.com/file/d/1XlAx48mKYfNLROJZVJUvTaBXNqpaPZcw/view
A. Pyayt. (2020, Accessed on 6 April 2021). 6 - Smartphones for rapid kits. Smartphone Based Medical Diagnostics.
M. M. J.-A. Simeoni, et al. (2019, Accessed on 11 April 2021). DeepWave: a recurrent neural-network for real-time acoustic imaging. Advances In Neural Information Processing Systems 32 (Nips 2019) 32(CONF). Available: https://infoscience.epfl.ch/record/273319
V. Naresh and N. Lee. (2021, Accessed on 11 April 2021). A review on biosensors and recent development of nanostructured materials-enabled biosensors. Sensors 21(4), 1109. Available: https://app.dimensions.ai/details/publication/pub.1135179358
X. Cai, et al. (2020, Accessed on 11 April 2021). One-shot ultraspectral imaging with reconfigurable metasurfaces. arXiv preprint. Available: https://arxiv.org/abs/2005.02689v2
Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.