Scholarlink

<p>Sweat analysis has significant potential in personalized health monitoring and exercise physiology assessment due to its non-invasive and real-time nature. However, the complexity of sweat composition limits the applicability of existing potentiometric or voltammetric single sensors and e-tongue systems in complex analysis, as they rely on the selective detection of a single target analyte using specific electrodes, followed by pattern recognition based on that information. To address this limitation, this study proposes a flexible impedimetric e-tongue system based on a sensor array and machine learning-based pattern recognition for the holistic assessment of artificial sweat, enabling the high-accuracy classification of six exercise-related physiological states. The system is constructed using a laser-induced graphene (LIG) interdigitated electrode array, integrated with multi-walled carbon nanotubes (MWCNTs) and metal oxide nanoparticles (MONPs) composite sensing films, ensuring high sensitivity and stability. Experimental results demonstrate that different sensing elements exhibit complementary responses to major sweat analytes (Na⁺, K⁺, lactic acid, and glucose), forming distinctive sweat fingerprint signals. Key parameters were extracted from impedance spectroscopy (IS) and further analyzed using principal component analysis (PCA) for features transformation and support vector machine (SVM) for classification, achieving an overall 97.69% classification accuracy in identifying exercise-related physiological states. Furthermore, the sensor array exhibited high sensitivity, a broad linear range, excellent repeatability (RSD ≤ 1.67%), reproducibility (RSD ≤ 2.9%), and stability over ~ 20 days, confirming its feasibility for sweat analysis. Compared to potentiometric and voltammetric single sensors and e-tongue systems, the proposed impedimetric e-tongue system does not rely on the selectivity of individual sensors but instead analyzes the overall impedance pattern, significantly enhancing its capability for complex biofluid analysis. In conclusion, this study presents a novel impedimetric e-tongue system as a potential solution for exercise monitoring, health assessment, and personalized medicine, providing new insights into the development of non-invasive sweat analysis technologies.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Exercise-related physiological state classification via a pattern-driven flexible impedimetric e-tongue using holistic sweat analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月10日

查看全文
  • Tianqi Lu,
  • Jiarui Fan,
  • Shiqi You,
  • Anurag Adiraju,
  • Yikang Guo,
  • Malak Talbi,
  • Hussamaldeen Jaradat,
  • Olfa Kanoun

摘要

Sweat analysis has significant potential in personalized health monitoring and exercise physiology assessment due to its non-invasive and real-time nature. However, the complexity of sweat composition limits the applicability of existing potentiometric or voltammetric single sensors and e-tongue systems in complex analysis, as they rely on the selective detection of a single target analyte using specific electrodes, followed by pattern recognition based on that information. To address this limitation, this study proposes a flexible impedimetric e-tongue system based on a sensor array and machine learning-based pattern recognition for the holistic assessment of artificial sweat, enabling the high-accuracy classification of six exercise-related physiological states. The system is constructed using a laser-induced graphene (LIG) interdigitated electrode array, integrated with multi-walled carbon nanotubes (MWCNTs) and metal oxide nanoparticles (MONPs) composite sensing films, ensuring high sensitivity and stability. Experimental results demonstrate that different sensing elements exhibit complementary responses to major sweat analytes (Na⁺, K⁺, lactic acid, and glucose), forming distinctive sweat fingerprint signals. Key parameters were extracted from impedance spectroscopy (IS) and further analyzed using principal component analysis (PCA) for features transformation and support vector machine (SVM) for classification, achieving an overall 97.69% classification accuracy in identifying exercise-related physiological states. Furthermore, the sensor array exhibited high sensitivity, a broad linear range, excellent repeatability (RSD ≤ 1.67%), reproducibility (RSD ≤ 2.9%), and stability over ~ 20 days, confirming its feasibility for sweat analysis. Compared to potentiometric and voltammetric single sensors and e-tongue systems, the proposed impedimetric e-tongue system does not rely on the selectivity of individual sensors but instead analyzes the overall impedance pattern, significantly enhancing its capability for complex biofluid analysis. In conclusion, this study presents a novel impedimetric e-tongue system as a potential solution for exercise monitoring, health assessment, and personalized medicine, providing new insights into the development of non-invasive sweat analysis technologies.