Smartphone Modulated Colorimetric Reader with Color Subtraction

Zhao, Yunfeng; Choi, Somi; Lou-Franco, Javier; Nelis, Joost L.D.; Zhou, Huiyu; Cao, Cuong; Campbell, Katrina; Elliott, Chris; Rafferty, Karen

doi:10.1109/sensors43011.2019.8956565

Cited by 9 publications

(15 citation statements)

References 22 publications

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“…Thus, for color intensity variation using LFA, it seems that two- point calibration or camera calibration is necessary. Some suggested methods exist to obtain such calibration and improve sensitivity [30,31,32,33]. One interesting concept is to use a color reference chart in order to stabilize color variations caused by built-in automatic image correction operations [30].…”

Section: Resultsmentioning

confidence: 99%

The Efficiency of Color Space Channels to Quantify Color and Color Intensity Change in Liquids, pH Strips, and Lateral Flow Assays with Smartphones

Nelis

Bura

Zhao

et al. 2019

Sensors

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Bottom-up, end-user based feed, and food analysis through smartphone quantification of lateral flow assays (LFA) has the potential to cause a paradigm shift in testing capabilities. However, most developed devices do not test the presence of and implications of inter-phone variation. Much discussion remains regarding optimum color space for smartphone colorimetric analyses and, an in-depth comparison of color space performance is missing. Moreover, a light-shielding box is often used to avoid variations caused by background illumination while the use of such a bulky add-on may be avoidable through image background correction. Here, quantification performance of individual channels of RGB, HSV, and LAB color space and ΔRGB was determined for color and color intensity variation using pH strips, filter paper with dropped nanoparticles, and colored solutions. LAB and HSV color space channels never outperformed the best RGB channels in any test. Background correction avoided measurement variation if no direct sunlight was used and functioned more efficiently outside a light-shielding box (prediction errors < 5%/35% for color/color intensity change). The system was validated using various phones for quantification of major allergens (i.e., gluten in buffer, bovine milk in goat milk and goat cheese), and, pH in soil extracts with commercial pH strips and LFA. Inter-phone variation was significant for LFA quantification but low using pH strips (prediction errors < 10% for all six phones compared). Thus, assays based on color change hold the strongest promise for end-user adapted smartphone diagnostics.

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Section: Resultsmentioning

confidence: 99%

The Efficiency of Color Space Channels to Quantify Color and Color Intensity Change in Liquids, pH Strips, and Lateral Flow Assays with Smartphones

Nelis

Bura

Zhao

et al. 2019

Sensors

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“…RGB values of the input image were converted to CieLab and HSV values, using OpenCV library . A previously developed algorithm was used as the prediction algorithm for color/intensity quantification with slight modifications . The algorithm is based on polynomial regression: where θ is the polynomial parameter vector, N is the order number, and X 1 , X 2 , and X 3 are the input color values of the channels used for any combination of three channels with X 1–3 being the respective single color channels of the color spaces.…”

Section: Materials and Methodsmentioning

confidence: 99%

“…37 A previously developed algorithm was used as the prediction algorithm for color/intensity quantification with slight modifications. 38 The algorithm is based on polynomial regression:…”

Section: Methodsmentioning

confidence: 99%

A Randomized Combined Channel Approach for the Quantification of Color- and Intensity-Based Assays with Smartphones

et al. 2020

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Quantification of colorimetric assays with smartphones is being increasingly reported. However, a complete characterization of the performance of existing color spaces and single-color channels for optimum color/intensity change quantification is absent. Moreover, it has not been ascertained if it is necessary to utilize existing color spaces to reach optimal assay quantification. In this study, a randomized channel approach was adapted utilizing all single channels from RGB, HSV, and CieLab color space and all nonrepeating random combinations of two and three channels of these color spaces. Assays based on color or intensity change using pH strips and gold or carbon black nanoparticle-containing paper strips were optimized using this approach. Several novel channel combinations showed great promise, in terms of prediction error and interphone variation reduction, outperforming RGB, HSV, and CieLab color spaces. These novel combinations were used in a custom-developed smartphone application that performed automated background subtraction and polynomial regression for the quantification of a lateral flow assay for the detection of goat milk adulteration with cow milk and for pH prediction in soil. For the lateral flow assay the channel combination BSA was found optimum (mean average error = 36% ± 6%; R 2 = 0.97). For the soil pH assay the channel combination RLC was found optimum (mean average error = 1.31% ± 0.02%; R 2 = 0.997). The study has shown that nonclassical channel combinations for colorimetric quantification of specific assays are very promising and should be considered for smartphone-based analysis.

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“…Moreover, the electrochemical signal is not affected by ambient illumination conditions and the color or turbidity of the sample. This is an extra advantage in the food safety field, where extracts often remain colored or exhibit a certain level of turbidity, which can interfere with optical measurements [ 20 , 76 ]. However, electrochemical detection can be affected by electrode surface fouling and/or poisoning caused by food matrix components, affecting the robustness of measurements.…”

Section: Portable Electrochemical Food Analyzersmentioning

confidence: 99%

ASSURED Point-of-Need Food Safety Screening: A Critical Assessment of Portable Food Analyzers

Jafari

Guercetti

Geballa-Koukoula

et al. 2021

Foods

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Standard methods for chemical food safety testing in official laboratories rely largely on liquid or gas chromatography coupled with mass spectrometry. Although these methods are considered the gold standard for quantitative confirmatory analysis, they require sampling, transferring the samples to a central laboratory to be tested by highly trained personnel, and the use of expensive equipment. Therefore, there is an increasing demand for portable and handheld devices to provide rapid, efficient, and on-site screening of food contaminants. Recent technological advancements in the field include smartphone-based, microfluidic chip-based, and paper-based devices integrated with electrochemical and optical biosensing platforms. Furthermore, the potential application of portable mass spectrometers in food testing might bring the confirmatory analysis from the laboratory to the field in the future. Although such systems open new promising possibilities for portable food testing, few of these devices are commercially available. To understand why barriers remain, portable food analyzers reported in the literature over the last ten years were reviewed. To this end, the analytical performance of these devices and the extent they match the World Health Organization benchmark for diagnostic tests, i.e., the Affordable, Sensitive, Specific, User-friendly, Rapid and Robust, Equipment-free, and Deliverable to end-users (ASSURED) criteria, was evaluated critically. A five-star scoring system was used to assess their potential to be implemented as food safety testing systems. The main findings highlight the need for concentrated efforts towards combining the best features of different technologies, to bridge technological gaps and meet commercialization requirements.

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Smartphone Modulated Colorimetric Reader with Color Subtraction

Cited by 9 publications

References 22 publications

The Efficiency of Color Space Channels to Quantify Color and Color Intensity Change in Liquids, pH Strips, and Lateral Flow Assays with Smartphones

The Efficiency of Color Space Channels to Quantify Color and Color Intensity Change in Liquids, pH Strips, and Lateral Flow Assays with Smartphones

A Randomized Combined Channel Approach for the Quantification of Color- and Intensity-Based Assays with Smartphones

ASSURED Point-of-Need Food Safety Screening: A Critical Assessment of Portable Food Analyzers

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