Smartphone-based analytical biosensors

Huang, Xiwei; Xu, Dandan; Chen, Jin; Liu, Jixuan; Li, Yangbo; Song, Jing; Ma, Xing; Guo, Jinhong

doi:10.1039/c8an01269e

Cited by 251 publications

(144 citation statements)

References 84 publications

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“…Resolutions comparable to scientific cameras were achieved, but intensity and color measurements were limited by a lack of camera control and factors including nonlinearity and white balance [32]. A full review of smartphone science is outside the scope of this work, and we instead refer the reader to a number of extensive reviews by other authors [35][36][37][38][39][40][41][42].…”

Section: Introductionmentioning

confidence: 99%

Standardized spectral and radiometric calibration of consumer cameras

Burggraaff¹,

Schmidt²,

Zamorano³

et al. 2019

Opt. Express

108

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Consumer cameras, particularly onboard smartphones and UAVs, are now commonly used as scientific instruments. However, their data processing pipelines are not optimized for quantitative radiometry and their calibration is more complex than that of scientific cameras. The lack of a standardized calibration methodology limits the interoperability between devices and, in the ever-changing market, ultimately the lifespan of projects using them. We present a standardized methodology and database (SPECTACLE) for spectral and radiometric calibrations of consumer cameras, including linearity, bias variations, read-out noise, dark current, ISO speed and gain, flat-field, and RGB spectral response. This includes golden standard ground-truth methods and do-it-yourself methods suitable for non-experts. Applying this methodology to seven popular cameras, we found high linearity in RAW but not JPEG data, inter-pixel gain variations >400% correlated with large-scale bias and read-out noise patterns, non-trivial ISO speed normalization functions, flat-field correction factors varying by up to 2.79 over the field of view, and both similarities and differences in spectral response. Moreover, these results differed wildly between camera models, highlighting the importance of standardization and a centralized database. accuracy of color measurements and their conversion to standard measures, such as the CIE 1931 XYZ and CIELAB color spaces, is limited by distortions in the observed colors [20] and differences in spectral response functions [21,[23][24][25].Extensive (spectro-)radiometric calibrations of consumer cameras are laborious and require specialized equipment and are thus not commonly performed [23,54,60]. A notable exception is the spectral and absolute radiometric calibration of a Raspberry Pi 3 V2 webcam by Pagnutti et al. [57], including calibrations of linearity, exposure stability, thermal and electronic noise, flat-field, and spectral response. Using this absolute radiometric calibration, digital values can be converted into SI units of radiance. However, the authors noted the need to characterize a large number of these cameras before the results could be applied in general. Moreover, certain calibrations are device-dependent and would need to be done separately on each device. Spectral and radiometric calibrations of seven cameras, including the Raspberry Pi, are given in [51]. These calibrations include dark current, flat-fielding, linearity, and spectral characterization. However, for the five digicams included in this work, JPEG data were used, severely limiting the quality and usefulness of these calibrations, as described above.Spectral characterizations are more commonly published since these are vital for quantitative color analysis. Using various methods, the spectral responses of several Canon [1,19,23,24,46,51,54,61], Nikon [1,13,23,24,54,[59][60][61][62], Olympus [23, 24, 51], Panasonic [46], SeaLife [13], Sigma [60], and Sony [1, 23, 46, 51, 61] digital cameras (digicams), as well as a number of smartpho...

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Section: Introductionmentioning

confidence: 99%

Standardized spectral and radiometric calibration of consumer cameras

Burggraaff¹,

Schmidt²,

Zamorano³

et al. 2019

Opt. Express

108

View full text Add to dashboard Cite

show abstract

“…Electrochemical analysis can typically be classified as either potentiometric, amperometric, or impedimetric where measures of voltage, current, and impedance are related to analyte concentrations, respectively. [71] A recent example of potentiometric sensing is the smartphone-interfaced chip for biological sex identification demonstrated by Deng et al [72] The device consisted of an external electrochemical chip that connected to a smartphone using microUSB, and was controlled by an application on the phone. The device used cyclic voltammetry to detect creatine kinase (CK) and alanine transaminase (ALT) via quantitative detection of NADH consumption.…”

Section: Tethered: Off-phone Biosensing With Local Data Processingmentioning

confidence: 99%

Biosensors for Personal Mobile Health: A System Architecture Perspective

Arumugam

Colburn

Sia

2019

Adv Materials Technologies

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Advances in mobile biosensors, integrating developments in materials science and instrumentation, are fueling an expansion in health data being collected and analyzed in decentralized settings. For example, semiconductor‐based sensors are enabling measurement of vital signs, and microfluidic‐based sensors are enabling measurement of biochemical markers. As biosensors for mobile health are becoming increasingly paired with smart devices, it can become critical for researchers to design biosensors—with appropriate functionalities and specifications—to work seamlessly with accompanying connected hardware and software. Here, recent research in biosensors, as well as current mobile health devices in use is described and classified into four distinct system architectures that take into account the biosensing and data processing functions required in personal mobile health devices. The path forward for integrating biosensors into smartphone‐based mobile health devices is also discussed.

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“…Recently, the smartphone-based electrochemiluminescence (ECL) sensor have been presented as a fast, low-cost and accurate alternative to expensive traditional instrumentation (such as the photomultiplier tube-based detectors) for analytical detection. [1,2] Smartphones devices are equipped with increasingly powerful processors for storage and analysis of data and have powerful data transmission capability. The use of these devices combined with certain accessory attachment (e. g., signal detectors) and advanced processing algorithms is a growing platform for different studies and applications, which in recent years, have focused on the prevention and monitoring of health.…”

Section: Introductionmentioning

confidence: 99%

Electrochemiluminescence Mechanisms Investigated with Smartphone‐Based Sensor Data Modeling, Parameter Estimation and Sensitivity Analysis

et al. 2020

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The present study introduces a unified framework combining a mechanistic model with a genetic algorithm (GA) for the parameter estimation of electrochemiluminescence (ECL) kinetics of the Ru(bpy)32+/TPrA system occurring in a smartphone‐based sensor. The framework allows a straightforward solution for simultaneous estimation of multiple parameters which can be, otherwise, time‐consuming and lead to non‐convergence. Model parameters are estimated by achieving a high correlation between the model prediction and the measured ECL intensity from the ECL sensor. The developed model is used to perform a sensitivity analysis (SA), which provides quantitative effects of the model parameters on the concentrations of chemical species involved in the system. The results demonstrate that the GA‐based parameter estimation and the SA approaches are effective in analyzing the kinetics of the ECL mechanism. Therefore, these approaches can be incorporated as analysis tools in the ECL kinetics study with practical application in the calibration of mechanistic models for any required sensing condition.

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Smartphone-based analytical biosensors

Abstract: With the rapid development, mass production, and pervasive distribution of smartphones in recent years, they have provided people with portable, cost-effective, and easy-to-operate platforms to build analytical biosensors for point-of-care (POC) applications and mobile health.

Cited by 251 publications

References 84 publications

Standardized spectral and radiometric calibration of consumer cameras

Standardized spectral and radiometric calibration of consumer cameras

Biosensors for Personal Mobile Health: A System Architecture Perspective

Electrochemiluminescence Mechanisms Investigated with Smartphone‐Based Sensor Data Modeling, Parameter Estimation and Sensitivity Analysis

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