3D-Printed PLA Hollow Microneedles Loaded with Chitosan Nanoparticles for Colorimetric Glucose Detection in Sweat Using Machine Learning

Biosensors play a central role in the early detection of abnormal glucose levels in individuals with diabetes; therefore, the development of less invasive systems is essential. Herein, a 3D-printed colorimetric biosensor combining microneedles and chitosan nanoparticles was developed for glucose det...

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Main Authors: Anastasia Skonta, Myrto G. Bellou, Haralambos Stamatis
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Biosensors
Subjects:
Online Access:https://www.mdpi.com/2079-6374/15/7/461
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author Anastasia Skonta
Myrto G. Bellou
Haralambos Stamatis
author_facet Anastasia Skonta
Myrto G. Bellou
Haralambos Stamatis
author_sort Anastasia Skonta
collection DOAJ
description Biosensors play a central role in the early detection of abnormal glucose levels in individuals with diabetes; therefore, the development of less invasive systems is essential. Herein, a 3D-printed colorimetric biosensor combining microneedles and chitosan nanoparticles was developed for glucose detection in sweat using machine learning. Briefly, hollow 3D-printed polylactic acid microneedles were constructed and loaded with chitosan nanoparticles encapsulating glucose oxidase, horseradish peroxidase, and the chromogenic substrate 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid), resulting in the formation of the chitosan nanoparticle−microneedle patches. Glucose detection was performed colorimetrically by first incubating the chitosan nanoparticle−microneedle patches with glucose samples of varying concentrations and then by using photographs of the top side of each microneedle and a color recognition application on a smartphone. The Random Sample Consensus algorithm was used to train a simple linear regression model to predict glucose concentrations in unknown samples. The developed biosensor system exhibited a good linear response range toward glucose (0.025−0.375 mM), a low limit of detection (0.023 mM), a limit of quantification (0.078 mM), high specificity, and recovery rates ranging between 86–112%. Lastly, the biosensor was applied to glucose detection in spiked artificial sweat samples, confirming the potential of the proposed methodology for glucose detection in real samples.
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institution Kabale University
issn 2079-6374
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publishDate 2025-07-01
publisher MDPI AG
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series Biosensors
spelling doaj-art-16a3518c49e049bd94be0d0d5b62afc82025-08-20T03:58:30ZengMDPI AGBiosensors2079-63742025-07-0115746110.3390/bios150704613D-Printed PLA Hollow Microneedles Loaded with Chitosan Nanoparticles for Colorimetric Glucose Detection in Sweat Using Machine LearningAnastasia Skonta0Myrto G. Bellou1Haralambos Stamatis2Laboratory of Biotechnology, Department of Biological Applications and Technologies, University of Ioannina, 45110 Ioannina, GreeceLaboratory of Biotechnology, Department of Biological Applications and Technologies, University of Ioannina, 45110 Ioannina, GreeceLaboratory of Biotechnology, Department of Biological Applications and Technologies, University of Ioannina, 45110 Ioannina, GreeceBiosensors play a central role in the early detection of abnormal glucose levels in individuals with diabetes; therefore, the development of less invasive systems is essential. Herein, a 3D-printed colorimetric biosensor combining microneedles and chitosan nanoparticles was developed for glucose detection in sweat using machine learning. Briefly, hollow 3D-printed polylactic acid microneedles were constructed and loaded with chitosan nanoparticles encapsulating glucose oxidase, horseradish peroxidase, and the chromogenic substrate 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid), resulting in the formation of the chitosan nanoparticle−microneedle patches. Glucose detection was performed colorimetrically by first incubating the chitosan nanoparticle−microneedle patches with glucose samples of varying concentrations and then by using photographs of the top side of each microneedle and a color recognition application on a smartphone. The Random Sample Consensus algorithm was used to train a simple linear regression model to predict glucose concentrations in unknown samples. The developed biosensor system exhibited a good linear response range toward glucose (0.025−0.375 mM), a low limit of detection (0.023 mM), a limit of quantification (0.078 mM), high specificity, and recovery rates ranging between 86–112%. Lastly, the biosensor was applied to glucose detection in spiked artificial sweat samples, confirming the potential of the proposed methodology for glucose detection in real samples.https://www.mdpi.com/2079-6374/15/7/461chitosan nanoparticlesmicroneedles3D printingPLAglucose oxidaseglucose
spellingShingle Anastasia Skonta
Myrto G. Bellou
Haralambos Stamatis
3D-Printed PLA Hollow Microneedles Loaded with Chitosan Nanoparticles for Colorimetric Glucose Detection in Sweat Using Machine Learning
Biosensors
chitosan nanoparticles
microneedles
3D printing
PLA
glucose oxidase
glucose
title 3D-Printed PLA Hollow Microneedles Loaded with Chitosan Nanoparticles for Colorimetric Glucose Detection in Sweat Using Machine Learning
title_full 3D-Printed PLA Hollow Microneedles Loaded with Chitosan Nanoparticles for Colorimetric Glucose Detection in Sweat Using Machine Learning
title_fullStr 3D-Printed PLA Hollow Microneedles Loaded with Chitosan Nanoparticles for Colorimetric Glucose Detection in Sweat Using Machine Learning
title_full_unstemmed 3D-Printed PLA Hollow Microneedles Loaded with Chitosan Nanoparticles for Colorimetric Glucose Detection in Sweat Using Machine Learning
title_short 3D-Printed PLA Hollow Microneedles Loaded with Chitosan Nanoparticles for Colorimetric Glucose Detection in Sweat Using Machine Learning
title_sort 3d printed pla hollow microneedles loaded with chitosan nanoparticles for colorimetric glucose detection in sweat using machine learning
topic chitosan nanoparticles
microneedles
3D printing
PLA
glucose oxidase
glucose
url https://www.mdpi.com/2079-6374/15/7/461
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