Non-enzymatic colorimetric glucose detection based on Au/Ag nanoparticles using smartphone and machine learning

Kılıç, Volkan; Mercan, Oyku Berfin; Tetik, Mehmet; Kap, Özlem; Horzum, Nesrin

doi:10.2116/analsci.21p253

Cited by 14 publications

(2 citation statements)

References 67 publications

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“…The regression models used are as follows. model_names = [ "Linear Regression", "Decision Tree", "Random Forest", "GradientBoostingRegressor" "K-Nearest Neighbor", " MLPRegressor ", " GaussianProcessRegressor ", "XGBoost", "CatBoost" ] Kilic et al used machine learning classification models when detecting non-enzymatic glucose using a mobile smart phone and achieved a success rate of 93% [12]. Regression models were used in the study in this paper.…”

Section: Methodsmentioning

confidence: 99%

Performance Comparisons of Machine Learning Methods of PLA-based Photochromic Material UV Sensor

2023

IJANSER

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A simple method for measuring of ultraviolet (UV) radiation or index values is introduced. In this study, which aims to use machine learning models to accurately analyze a changing color scale and make predictions about the magnitude of the external stimulus that causes color change, the photochromic Polylactic acid (PLA) material that changes color under UV light was video recorded with a smartphone camera. Then, by interpreting the data sets created from these images with machine learning models, a relationship was established between the current applied to the UV light source and the color. Video images taken with the smartphone camera were augmented with screen captures of 25 consecutive seconds, enabling the regression models used to make more accurate predictions. 9 different regression models were used, their performances were evaluated according to cross-validation results. Then, model performances were improved by using appropriate hyperparameters. Better accuracies were achieved especially in CatBoost Regression model. The findings of the study showed that UV intensity or index values can be determined with high accuracy with the existing smart phone camera without the need for any device.

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Section: Methodsmentioning

confidence: 99%

Performance Comparisons of Machine Learning Methods of PLA-based Photochromic Material UV Sensor

2023

IJANSER

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“…[98] The variety of the dataset is the measurement of the convergence of the distribution of data samples to the overall population. Improving the variety of the dataset is mostly relied on the sampling procedure, in which the LG G6 Semiquantitative [194] percentage of a class of samples should be approximately the actual percentage of that class. By increasing the quality of the dataset, the accuracy of the algorithm can be improved.…”

Section: Accuracy Improvement Strategiesmentioning

confidence: 99%

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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Colorimetric detection of H2O2 with Fe3O4@Chi nanozyme modified µPADs using artificial intelligence

et al. 2022

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Non-enzymatic colorimetric glucose detection based on Au/Ag nanoparticles using smartphone and machine learning

Cited by 14 publications

References 67 publications

Performance Comparisons of Machine Learning Methods of PLA-based Photochromic Material UV Sensor

Performance Comparisons of Machine Learning Methods of PLA-based Photochromic Material UV Sensor

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

Colorimetric detection of H2O2 with Fe3O4@Chi nanozyme modified µPADs using artificial intelligence

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