A Chemometric-Assisted Colorimetric-Based Inexpensive Paper Biosensor for Glucose Detection

Kishnani, Vinay; Kumari, Shrishti; Gupta, Ankur

doi:10.3390/bios12111008

Cited by 14 publications

(3 citation statements)

References 34 publications

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“…The region between the outer edge of the inner circle and the inner edge of the outer circle was utilized as a boundary to establish a hydrophobic barrier. For a comprehensive description of the fabrication process, previous publication by the group can be referred …”

Section: Methodsmentioning

confidence: 99%

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Predictive Framework Development for User-Friendly On-Site Glucose Detection

Kishnani,

Gupta

2023

ACS Appl. Bio Mater.

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This study explores a smartphone-based spot detection framework for glucose in a rapid, simple, and affordable paper-based analytical device (PAD), which employs machine-learning algorithms to estimate various glucose concentrations. Herein, two different detection mixtures were chosen with chitosan (C) and without chitosan (WC) for the color change analysis. Being a biopolymer, chitosan improves the analytical performance of PADs when used with a chromogenic agent. Moreover, the influence of the illumination conditions and camera optics on the professed color of glucose strips was observed by choosing various illumination conditions and different smartphones. Hence, this study focuses on developing a framework for smartphone-based simple and user-friendly spot-based glucose detection (with a concentration range of 10–40 mM) at any illumination conditions and in any direction of illumination. Additionally, the combination of color spaces and machine-learning algorithms was applied for the signal enhancement. It was observed that the machine learning classifiers, cubic support vector machine (SVM) and narrow neutral network show higher accuracy for the WC samples, which are 92.7 and 92.3%, respectively. The samples with chitosan show higher accuracy for the linear discriminant and quadratic SVM classifiers, which are 94.1 and 93.9%, respectively. Simultaneously, cubic SVM shows ∼93% accuracy for both cases. In order to assess the performance of the devices, a blind test was also conducted. This study demonstrates the potential of the developed system for initial disease screening at the user end. By incorporating machine learning techniques, the platform can provide reliable and accurate results, thus paving the way for estimating the accuracy of the results for improved initial healthcare screening and diagnosis of any disease.

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Section: Methodsmentioning

confidence: 99%

“…For a comprehensive description of the fabrication process, previous publication by the group can be referred. 27 Image Capturing. In order to achieve high classification performance, machine learning-based approaches require training with a suitable data set.…”

Section: ■ Experimental Setupmentioning

confidence: 99%

Predictive Framework Development for User-Friendly On-Site Glucose Detection

Kishnani,

Gupta

2023

ACS Appl. Bio Mater.

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“…Common detection methods for H 2 O 2 and Glu include spectrophotometry, chemiluminescence, titrimetric analysis, colorimetry, and chromatography; however, these methods generally suffer from low sensitivity, cumbersome operation steps, long process durations, and high costs, limiting their wide application [21][22][23]. The recently developed electrochemical method has the advantages of high specificity and sensitivity for real-time monitoring; it is also inexpensive and portable and involves a simple operation procedure [24,25].…”

Section: Introductionmentioning

confidence: 99%

Catalytic Modification of Porous Two-Dimensional Ni-MOFs on Portable Electrochemical Paper-Based Sensors for Glucose and Hydrogen Peroxide Detection

Yang

Yin

et al. 2023

Biosensors

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Rapid and accurate detection of changes in glucose (Glu) and hydrogen peroxide (H2O2) concentrations is essential for the predictive diagnosis of diseases. Electrochemical biosensors exhibiting high sensitivity, reliable selectivity, and rapid response provide an advantageous and promising solution. A porous two-dimensional conductive metal–organic framework (cMOF), Ni-HHTP (HHTP = 2,3,6,7,10,11-hexahydroxytriphenylene), was prepared by using a one-pot method. Subsequently, it was employed to construct enzyme-free paper-based electrochemical sensors by applying mass-producing screen-printing and inkjet-printing techniques. These sensors effectively determined Glu and H2O2 concentrations, achieving low limits of detection of 1.30 μM and 2.13 μM, and high sensitivities of 5573.21 μA μM−1 cm−2 and 179.85 μA μM−1 cm−2, respectively. More importantly, the Ni-HHTP-based electrochemical sensors showed an ability to analyze real biological samples by successfully distinguishing human serum from artificial sweat samples. This work provides a new perspective for the use of cMOFs in the field of enzyme-free electrochemical sensing, highlighting their potential for future applications in the design and development of new multifunctional and high-performance flexible electronic sensors.

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An Optoelectronic Sensing Real‐Time Glucose Detection Film Using Photonic Crystal Enhanced Rare Earth Fluorescence and Additive Manufacturing

Ju,

Wang,

Chen

et al. 2025

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In this research, a novel detection method employing rare‐earth upconversion nanoparticle (UCNP) as the core, coated with MnO2 nanosheets is designed, which formed a color and fluorescence dual‐responsive UCNP composite material, MnO2‐modified NaYF4:Yb,Tm@NaYF4. By enabling both colorimetric and fluorescence methods simultaneously, this composite material allows for the detection of glucose concentration under different conditions, while exhibiting strong resistance to environmental interference, chemical stability, and accuracy. To further enhance the sensitivity of the detection method, a photonic crystals (PCs)‐PDMS array where polymethyl methacrylate PCs are deposited onto a substrate composed of PDMS‐glass slice with hydrophobic surfaces is developed. This array can serve as a substrate that specifically reflected blue light while allowing other colors of light to pass through, which effectively reduced background signal interference and improved detection sensitivity (1.2 µm) with a wider linear range (20–800 µm). Finally, a portable fluorescence intensity detection device is designed to enhance the portability of the platform. Numerous experimental results demonstrated that this research significantly improved the sensitivity of glucose detection, providing new research directions for the field of fluid biomarker detectio.

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A Chemometric-Assisted Colorimetric-Based Inexpensive Paper Biosensor for Glucose Detection

Cited by 14 publications

References 34 publications

Predictive Framework Development for User-Friendly On-Site Glucose Detection

Predictive Framework Development for User-Friendly On-Site Glucose Detection

Catalytic Modification of Porous Two-Dimensional Ni-MOFs on Portable Electrochemical Paper-Based Sensors for Glucose and Hydrogen Peroxide Detection

An Optoelectronic Sensing Real‐Time Glucose Detection Film Using Photonic Crystal Enhanced Rare Earth Fluorescence and Additive Manufacturing

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