A machine learning-based multimodal electrochemical analytical device based on eMoSx-LIG for multiplexed detection of tyrosine and uric acid in sweat and saliva

Kammarchedu, Vinay; Butler, Derrick; Ebrahimi, Aida

doi:10.1016/j.aca.2022.340447

Cited by 29 publications

(16 citation statements)

References 57 publications

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“…There are a variety of machine learning models presented in the literature, each with broad, overlapping applications in the wearable space, which often make it hard to select the optimal algorithm to use. For quantifying chemicals in sweat, CNNs can measure lactate with an F1 score of 0.990, decision trees can measure glucose with a root mean squared of 0.1 mg/dL, KNNs can improve drifting errors in cortisol detection, and KNNs can measure tyrosine and uric acid (Figure d) . On-body sensors have diagnosed depression from a random forest algorithm, emotional states from support vector machines, , and stress from logistical regression .…”

Section: Data Processing For Wearable Sweat Sensorsmentioning

confidence: 99%

“…For quantifying chemicals in sweat, CNNs can measure lactate with an F1 score of 0.990, 612 decision trees can measure glucose with a root mean squared of 0.1 mg/dL, 632 KNNs can improve drifting errors in cortisol detection, 633 and KNNs can measure tyrosine and uric acid (Figure 41d). 619 On-body sensors have diagnosed depression from a random forest algorithm, 610 emotional states from support vector machines, 611,634 and stress from logistical regression. 635 From measuring chemicals in the sweat to psychological states, the use of different ML algorithms for similar problems highlights an important question: does the specific ML architecture matter when The simple answer is that ML models do not create the final trends in the input variables; rather, ML is a tool that connects information from the input-space to an observable output, if such a connection exists.…”

Section: Model Selectionmentioning

confidence: 99%

“…Copyright 2020 Rodríguez-Pérez et al d , Flowchart for extracting features from electrochemical measurements, training a model, and predicting analyte concentrations. Reproduced with permission from ref . Copyright 2022 Elsevier.…”

Section: Data Processing For Wearable Sweat Sensorsmentioning

confidence: 99%

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Skin-Interfaced Wearable Sweat Sensors for Precision Medicine

Min

et al. 2023

Chem. Rev.

200

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Wearable sensors hold great potential in empowering personalized health monitoring, predictive analytics, and timely intervention toward personalized healthcare. Advances in flexible electronics, materials science, and electrochemistry have spurred the development of wearable sweat sensors that enable the continuous and noninvasive screening of analytes indicative of health status. Existing major challenges in wearable sensors include: improving the sweat extraction and sweat sensing capabilities, improving the form factor of the wearable device for minimal discomfort and reliable measurements when worn, and understanding the clinical value of sweat analytes toward biomarker discovery. This review provides a comprehensive review of wearable sweat sensors and outlines stateof-the-art technologies and research that strive to bridge these gaps. The physiology of sweat, materials, biosensing mechanisms and advances, and approaches for sweat induction and sampling are introduced. Additionally, design considerations for the system-level development of wearable sweat sensing devices, spanning from strategies for prolonged sweat extraction to efficient powering of wearables, are discussed. Furthermore, the applications, data analytics, commercialization efforts, challenges, and prospects of wearable sweat sensors for precision medicine are discussed.

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Section: Data Processing For Wearable Sweat Sensorsmentioning

confidence: 99%

Section: Model Selectionmentioning

confidence: 99%

See 1 more Smart Citation

Skin-Interfaced Wearable Sweat Sensors for Precision Medicine

Min

et al. 2023

Chem. Rev.

200

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“…[236] Kammarchedu et al demonstrated 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 UA in sweat and saliva. [237] Park et al proposed an all-in-one electroanalytical device (AED), a miniaturized electronic POC device integrated with the most used electroanalytical techniques, such as amperometric, voltammetric, potentiometric, conductometric, and impedimetric techniques. [238] In simultaneous sensing, less solution is needed for analysis, enabling the practical use of matrixes that may be difficult to harvest, such as sweat or breath.…”

Section: Integrated Sensing Systems and Simultaneous Detection Of Bio...mentioning

confidence: 99%

Analyzing Electrochemical Sensing Fundamentals for Health Applications

Alam,

Mitea,

Howlader

et al. 2023

Advanced Sensor Research

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Humans continuously interact with physical, chemical, and biological environments that influence their health, safety, and quality of life. Sensing devices, such as electrochemical sensors that translate environmental qualities into electrical signals, are crucial for detecting biomarker concentrations in various biofluids. However, the understanding of electrochemical sensing is often incomplete, necessitating further study of chemical reactions and sensor‐electrode interactions for healthcare applications. This review analyzes crucial topics in chemical reactions in electrochemical sensing environments. First, the dynamics of chemical energy, the roles of acidic and alkaline fluids, chemical reaction tendencies, thermodynamic equilibria, Gibbs free energy, water dissociation, and the pH scale are discussed. Sensor materials or biomarkers undergo oxidation and reduction reactions in electrochemical sensing. Oxygen‐derived radicals and nonradical reactive species significantly influence biochemical reactions, cellular responses, and clinical outcomes. Then, the review delves into the impact of oxidation reduction reactions on human pathophysiology, redox reactions in hemoglobin, redox environments in human serum albumin and cells/tissues, and thermodynamics of biological redox reactions. Finally, recent advances in electrochemical techniques are presented and research challenges and future perspectives in electrochemical sensing for health applications are addressed.

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“…Classification algorithms can be used in sensing applications to identify a specific compound among a matrix of other compounds. [18][19][20][21] Regression algorithms can be used in sensing applications to identify a quantitative value over a given range. [22][23][24][25] Regression algorithms are more suited to sensing applications when the measured output is a numerical value from a range of possible values such as for pH, temperature, alkalinity, or the concentration of an analyte.…”

Section: Introductionmentioning

confidence: 99%

Modeling Biodegradable Free Chlorine Sensor Performance Using Artificial Neural Networks

Siddiqui,

Deen

2023

Adv Materials Technologies

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Electrochemical sensors are used to measure target analytes in water, meat, fruits, or vegetables, to ensure their safety, security, and integrity for human use. In this paper, a solution‐based fabrication process is presented for a biodegradable electrochemical free chlorine sensor using asparagine that is functionalized onto graphene oxide (GO). An ink solution of the GO functionalized with asparagine is synthesized, and then deposited onto a screen‐printed carbon electrode (SPCE) using a spin coater. The sensor shows high a sensitivity of 0.30 µA ppm−1 over a linear range of 0–8 ppm with a hysteresis‐limited resolution of 0.2 ppm after achieving a steady state at 50 s. From the development and testing of the free chlorine sensor, over 9000 datapoints are collected and used for training an artificial neural network (ANN) model to quantify and characterize the factors affecting the free chlorine sensor's performance, and model its degradation characteristics. The model reports a low mean absolute error (MAE) of 0.1603 and a high model accuracy with a Pearson correlation coefficient (PCC) of 0.9950, demonstrating that these degradation characteristics can be modeled and be used to compensate the degraded performance characteristics of the free chlorine sensors.

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A machine learning-based multimodal electrochemical analytical device based on eMoSx-LIG for multiplexed detection of tyrosine and uric acid in sweat and saliva

Cited by 29 publications

References 57 publications

Skin-Interfaced Wearable Sweat Sensors for Precision Medicine

Skin-Interfaced Wearable Sweat Sensors for Precision Medicine

Analyzing Electrochemical Sensing Fundamentals for Health Applications

Modeling Biodegradable Free Chlorine Sensor Performance Using Artificial Neural Networks

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