Quantifying colorimetric tests using a smartphone app based on machine learning classifiers

Solmaz, Mehmet; Mutlu, Ali; Alankuş, Gazihan; Kılıç, Volkan; Bayram, Abdullah; Horzum, Nesrin

doi:10.1016/j.snb.2017.08.220

Cited by 110 publications

(77 citation statements)

References 25 publications

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“…Semi-quantitative assays have drawn a lot of attention for computer-aided colourimetric quantification [10], [11], [13], [21]. [10] provided the semi-quantitative colourimetric quantification of fifteen pH levels (0-14) or classes.…”

Section: (B)mentioning

confidence: 99%

“…Although the Quantofix Peroxide 100 is supposed to classify only six concentration levels of H2O2 (0, 1, 3, 10, 30 and 100 ppm), [11] added five secondary concentration levels (0.5, 2, 6.5, 20 and 65 ppm) to increase the number of classes. In the absence of an elaborate description, we assume that the images of the test strips containing corresponding colors for these secondary classes can be considered as synthetic data, which may have reliability issue in a real-life scenario.…”

Section: (B)mentioning

confidence: 99%

“…In the literature, most of the work considers the assay as a single object/ sample [11], [21]. [21] used paper test strips [37] capable of measuring a single concentration using a single color pad.…”

Section: )mentioning

confidence: 99%

“…[21] used paper test strips [37] capable of measuring a single concentration using a single color pad. [11] used paper strips to quantify H2O2 using test strips of Quantofix Peroxide 100. This test strip [11] can provide a semi-quantitative measurement of H2O2 using a single color pad.…”

Section: )mentioning

confidence: 99%

“…[11] used paper strips to quantify H2O2 using test strips of Quantofix Peroxide 100. This test strip [11] can provide a semi-quantitative measurement of H2O2 using a single color pad. Both of these assays [11], [21] are for rapid test reading.…”

Section: )mentioning

confidence: 99%

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Assay Type Detection Using Advanced Machine Learning Algorithms

Tania

Lwin

Shabut

et al. 2019

2019 13th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)

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The colourimetric analysis has been used in diversified fields for years. This paper provides a unique overview of colourimetric tests from the perspective of computer vision by describing different aspects of a colourimetric test in the context of image processing, followed by an investigation into the development of a colorimetric assay type detection system using advanced machine learning algorithms. To the best of our knowledge, this is the first attempt to define colourimetric assay types from the eyes of a machine and perform any colorimetric test using deep learning. This investigation utilizes the state-of-the-art pre-trained models of Convolutional Neural Network (CNN) to perform the assay type detection of an enzyme-linked immunosorbent assay (ELISA) and lateral flow assay (LFA). The ELISA dataset contains images of both positive and negative samples, prepared for the plasmonic ELISA based TB-antigen specific antibody detection. The LFA dataset contains images of the universal pH indicator paper of eight pH levels. It is noted that the pre-trained models offered 100% accurate visual recognition for the assay type detection. Such detection can assist novice users to initiate a colorimetric test using his/her personal digital devices. The assay type detection can also aid in calibrating an image-based colorimetric classification.

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Section: (B)mentioning

confidence: 99%

Section: (B)mentioning

confidence: 99%

Section: )mentioning

confidence: 99%

Section: )mentioning

confidence: 99%

Section: )mentioning

confidence: 99%

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Assay Type Detection Using Advanced Machine Learning Algorithms

Tania

Lwin

Shabut

et al. 2019

2019 13th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)

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Digitalization of Colorimetric Sensor Technologies for Food Safety

Mazur,

Han,

Tjandra

et al. 2024

Advanced Materials

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Colorimetric sensors play a crucial role in promoting on‐site testing, enabling the detection and/or quantification of various analytes based on changes in colour. These sensors offer several advantages, such as simplicity, cost‐effectiveness, and visual readouts, making them suitable for a wide range of applications, including food safety and monitoring. A critical component in portable colorimetric sensors involves their integration with colour models for effective analysis and interpretation of output signals. The most commonly used models include CIELAB (Commission Internationale de l'Eclairage), RGB (Red, Green, Blue), and HSV (Hue, Saturation, Value). This review outlines the use of colour models via digitalisation in sensing applications within the food safety and monitoring field. Additionally, challenges, future directions, and considerations are discussed, highlighting a significant gap in integrating a comparative analysis towards determining the colour model that results in the highest sensor performance. The aim of this review is to underline the potential of this integration in mitigating the global impact of food spoilage and contamination on health and the economy, proposing a multidisciplinary approach to harness the full capabilities of colorimetric sensors in ensuring food safety.This article is protected by copyright. All rights reserved

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Engineering Strategies for Advancing Optical Signal Outputs in Smartphone‐Enabled Point‐of‐Care Diagnostics

Fan

Liu

Miao

et al. 2023

Advanced Intelligent Systems

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The use of smartphone‐based analysis systems has been increasing over the past few decades. Among the important reasons for its popularity are its ubiquity, increasing computing power, relatively low cost, and capability to acquire and process data simultaneously in a point‐of‐need fashion. Furthermore, smartphones are equipped with various sensors, especially a complementary metal–oxide–semiconductor (CMOS) sensor. The high sensitivity of the CMOS sensor allows smartphones to be used as a colorimeter, fluorimeter, and spectrometer, constituting the essential part of point‐of‐care testing contributing to E‐health and beyond. However, despite its myriads of merits, smartphone‐based diagnostic devices still face many challenges, including high susceptibility to illumination conditions, difficulty in adapter uniformization, low interphone repeatability, and et al. These problems may hinder smartphone‐enabled diagnosis from passing the FDA regulations of medical devices. This review discusses the design and application of current smartphone‐based diagnostic devices and highlights challenges associated with existent methods and perspectives on how to deal with those challenges from engineering aspects on constant color signal acquisition, including smartphone adapter design, color space transformation, machine learning classification, and color correction.

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Quantifying colorimetric tests using a smartphone app based on machine learning classifiers

Cited by 110 publications

References 25 publications

Assay Type Detection Using Advanced Machine Learning Algorithms

Assay Type Detection Using Advanced Machine Learning Algorithms

Digitalization of Colorimetric Sensor Technologies for Food Safety

Engineering Strategies for Advancing Optical Signal Outputs in Smartphone‐Enabled Point‐of‐Care Diagnostics

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