A machine learning-based multimodal electrochemical analytical device based on eMoSx-LIG for multiplexed detection of tyrosine and uric acid in sweat and saliva
Abstract
Multiplexed detection of biomolecules is of great value in various fields, from disease diagnosis to food safety and environmental monitoring. However, accurate and multiplexed analyte detection is challenging to achieve in mixtures using a single device/material. In this paper, we demonstrate a machine learning (ML)-powered multimodal analytical device based on a single sensing material made of electrodeposited molybdenum polysulfide (eMoS x) on laser induced graphene (LIG) for multiplexed detection of tyrosine (TYR) and uric acid (UA) in sweat and saliva. Electrodeposition of MoS x shows an increased electrochemically active surface area (ECSA) and heterogeneous electron transfer rate constant, k 0. Features are extracted from the electrochemical data in order to train ML models to predict the analyte concentration in the sample (both singly spiked and mixed samples). Different ML architectures are explored to optimize the sensing performance. The optimized ML-based multimodal analytical system offers a limit of detection (LOD) that is two orders of magnitude better than conventional approaches which rely on single peak analysis. A flexible and wearable sensor patch is also fabricated and validated on-body, achieving detection of UA and TYR in sweat over a wide concentration range. While the performance of the developed approach is demonstrated for detecting TYR and UA using eMoS x-LIG sensors, it is a general analytical methodology and can be extended to a variety of electrochemical sensors to enable accurate, reliable, and multiplexed sensing.
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Features extracted from electrochemical data were utilized to train ML models for predicting analyte concentrations in both single spiked and mixed samples.
“Features extracted from the electrochemical data were utilized to train ML models for predicting analyte concentrations in both single spiked and mixed samples.”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [21]
Electrodeposition of MoSx resulted in increased electrochemically active surface area and heterogeneous electron transfer rate constant.
“The electrodeposition of MoSx resulted in an increased electrochemically active surface area and heterogeneous electron transfer rate constant.”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [21]
An ML-based multimodal electrochemical analytical device utilizing eMoSx-Laser Induced Graphene was used for multiplexed detection of tyrosine and uric acid in sweat and saliva.
“For instance, an ML-based multimodal electrochemical analytical device, utilizing eMoSx-Laser Induced Graphene, was used for the multiplexed detection of tyrosine and uric acid in sweat and saliva [21].”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [21]
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- Authors
- Kammarchedu, V., Butler, D., & Ebrahimi, A.
- Affiliations
- Pennsylvania State University (US)
- Corresponding author
- Ebrahimi, Aida
- Venue
- Analytica Chimica Acta
- Publisher
- Elsevier BV
- Published
- 2022-11
- Volume
- 1232
- Pages
- 340447
- ISSN
- 0003-2670 (ISSN-L)
- OpenAlex
- W4298009637
- Cited by
- 79
- References
- 60
- Citations by year
- 2026: 24 · 2025: 20 · 2024: 18 · 2023: 16 · 2022: 1
- FWCI
- 3.76
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Analytica Chimica Acta (publishedVersion)
- PubMed (publishedVersion)
- ECSarXiv (OSF Preprints) (submittedVersion)
- Funders
- Pennsylvania State University
- National Science Foundation (2113864)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electrochemical sensors and biosensors
- Physical Sciences › Chemistry › Electrochemistry › Electrochemical Analysis and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Advanced biosensing and bioanalysis techniques
- Keywords
- laser-induced graphene, tyrosine detection, uric acid detection, sweat analysis, saliva testing, electrodeposition, flexible sensors, on-body monitoring
- MeSH terms
- Machine Learning, Smart Materials, Graphite, Lasers, Molybdenum, Saliva, Sweat, Tyrosine, Uric Acid, Biosensing Techniques, Electrochemical Techniques
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