A Portable Smartphone-based Platform with an Offline Image-processing Tool for the Rapid Paper-based Colorimetric Detection of Glucose in Artificial Saliva

Golcez, Tansu; Kılıç, Volkan; Şen, Mustafa

doi:10.2116/analsci.20p262

Cited by 27 publications

(14 citation statements)

References 37 publications

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“…The presence of light reflecting on the liquid samples during the photography could have resulted in some discrepancies in the extracted features. Microfluidic paper-based analytical devices (μPADs) are paperbased biosensors developed for disease diagnosis and can be used in photography to improve the ease of use and performance of the model [55,56]. With a better ROI, the feature extraction values can give a more accurate result, thus training models with better recognition.…”

Section: Resultsmentioning

confidence: 99%

Machine learning-aided evaluation of oxidative strength of cold atmospheric plasma-treated water

Irmak,

Ozdemir,

Ozdemir

et al. 2024

Biomed. Phys. Eng. Express

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Plasma medicine is gaining attraction in the medical field, particularly the use of cold atmospheric plasma (CAP) in biomedicine. The chemistry of the plasma is complex, and the reactive oxygen species (ROS) within it are the basis for the biological effect of CAP on the target. Understanding how the oxidative power of ROS responds to diverse plasma parameters is vital for standardizing the effective application of CAP. The proven applicability of machine learning (ML) in the field of medicine is encouraging, as it can also be applied in the field of plasma medicine to correlate the oxidative strength of plasma-treated water (PTW) according to different parameters. In this study, plasma-treated water was mixed with potassium iodide-starch reagent for color formation that could be linked to the oxidative capacity of PTW. Corresponding images were captured resulting from the exposure of the color-forming agent to water treated with plasma for different time points. Several ML models were trained to distinguish the color changes sourced by the oxidative strength of ROS. The AdaBoost Classifier (ABC) algorithm demonstrated better performance among the classification models used by extracting color-based features from the images. Our results, with a test accuracy of 63.5%, might carry a potential for future standardization in the field of plasma medicine with an automated system that can be created to interpret the oxidative properties of ROS in different plasma treatment parameters via ML.

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Section: Resultsmentioning

confidence: 99%

Machine learning-aided evaluation of oxidative strength of cold atmospheric plasma-treated water

Irmak,

Ozdemir,

Ozdemir

et al. 2024

Biomed. Phys. Eng. Express

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show abstract

“…Since the 1960s, portable electrochemical glucose meters have improved the quality of life of many people who have diabetes [173]. It has been demonstrated that regular remote reporting of results using SbSs can improve patients' outcomes [174]. Older individuals tend to use their smartphones for various social and nonsocial reasons, and contrary to adolescents, elders are not associated with problematic smartphone usage [175].…”

Section: Potential Social Impact In Diagnostics For Elderly Individualsmentioning

confidence: 99%

Best practices and current implementation of emerging smartphone-based (bio)sensors - Part 2: Development, validation, and social impact

Geballa-Koukoula¹,

Ross²,

Bosman³

et al. 2023

TrAC Trends in Analytical Chemistry

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“…It is also the most easily available body fluid in the human body. Therefore, saliva has also become a popularly studied glucose monitoring biological liquid ( Golcez et al, 2021 ). Current salivary glucose levels have been shown to positively correlate with blood glucose levels ( Bergenstal et al, 2018 ).…”

Section: Background Knowledge For Glucose Sensorsmentioning

confidence: 99%

Electrospun nanofiber-based glucose sensors for glucose detection

Zhang

Liu

et al. 2022

Front. Chem.

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Diabetes is a chronic, systemic metabolic disease that leads to multiple complications, even death. Meanwhile, the number of people with diabetes worldwide is increasing year by year. Sensors play an important role in the development of biomedical devices. The development of efficient, stable, and inexpensive glucose sensors for the continuous monitoring of blood glucose levels has received widespread attention because they can provide reliable data for diabetes prevention and diagnosis. Electrospun nanofibers are new kinds of functional nanocomposites that show incredible capabilities for high-level biosensing. This article reviews glucose sensors based on electrospun nanofibers. The principles of the glucose sensor, the types of glucose measurement, and the glucose detection methods are briefly discussed. The principle of electrospinning and its applications and advantages in glucose sensors are then introduced. This article provides a comprehensive summary of the applications and advantages of polymers and nanomaterials in electrospun nanofiber-based glucose sensors. The relevant applications and comparisons of enzymatic and non-enzymatic nanofiber-based glucose sensors are discussed in detail. The main advantages and disadvantages of glucose sensors based on electrospun nanofibers are evaluated, and some solutions are proposed. Finally, potential commercial development and improved methods for glucose sensors based on electrospinning nanofibers are discussed.

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A Portable Smartphone-based Platform with an Offline Image-processing Tool for the Rapid Paper-based Colorimetric Detection of Glucose in Artificial Saliva

Cited by 27 publications

References 37 publications

Machine learning-aided evaluation of oxidative strength of cold atmospheric plasma-treated water

Machine learning-aided evaluation of oxidative strength of cold atmospheric plasma-treated water

Best practices and current implementation of emerging smartphone-based (bio)sensors - Part 2: Development, validation, and social impact

Electrospun nanofiber-based glucose sensors for glucose detection

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