Machine Learning Assisted-Intelligent Lactic Acid Monitoring in Sweat Supported by a Perspiration-Driven Self-Powered Sensor.

Journal: Nano letters
PMID: 39909470

Abstract

Lactic acid has aroused increasing attention due to its close association with serious diseases. A real-time, dynamic, and intelligent detection method is vital for sensitive detection of lactic acid. Here, a machine learning (ML)-assisted perspiration-driven self-powered sensor (PDS sensor) is fabricated using Ni-ZIF-8@lactate oxidase and pyruvate oxidase (Ni-ZIF-8@LOx&POx)/laser-induced graphene (LIG), bilirubin oxidase (BOD)/LIG, and a microchannel for highly sensitive and real-time monitoring of lactic acid in sweat. Driven by the oxidation reaction of lactic acid, PDS sensors exhibit excellent sensitivity, a wide detection range, good reproducibility, and excellent selectivity for lactic acid detection in sweat. When subjects with different body mass index (BMI) undergo aerobic or anaerobic exercise or maintain a sedentary state, PDS sensors can monitor lactic acid in sweat wirelessly and in real-time. Moreover, a ML algorithm was employed to assist PDS sensors to detect lactic acid in the subjects' sweat with a high prediction accuracy of 96.0%.

Authors

  • Jing Xu
    First Department of Infectious Diseases, The First Affiliated Hospital of China Medical University, Shenyang, China.
  • Yujin Li
    College of Chemistry and Chemical Engineering, Xinyang Normal University, Dabie Mountain Laboratory, Xinyang 464000, China.
  • Futing Wang
    Molecular Science and Biomedicine Laboratory, State Key Laboratory for Chemo/Bio-Sensing and Chemometrics, College of Material Science and Engineering, College of Chemistry and Chemical Engineering, College of Biology, Hunan University, Changsha 410082, China.
  • Wangchen Li
    College of Pipeline and Civil Engineering, China University of Petroleum, Shandong 266580, China.
  • Jiajun Zhan
    Molecular Science and Biomedicine Laboratory, State Key Laboratory for Chemo/Bio-Sensing and Chemometrics, College of Material Science and Engineering, College of Chemistry and Chemical Engineering, College of Biology, Hunan University, Changsha 410082, China.
  • Suping Deng
    Hunan Key Laboratory of Typical Environmental Pollution and Health Hazards, School of Public Health, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.
  • Changxiao Song
    Molecular Science and Biomedicine Laboratory, State Key Laboratory for Chemo/Bio-Sensing and Chemometrics, College of Material Science and Engineering, College of Chemistry and Chemical Engineering, College of Biology, Hunan University, Changsha 410082, China.
  • Hongfen Yang
    Hunan Key Laboratory of Typical Environmental Pollution and Health Hazards, School of Public Health, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.
  • Ren Cai
    School of Electronics and Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China.
  • Weihong Tan
    Molecular Science and Biomedicine Laboratory (MBL), State Key Laboratory of Chemo/Bio-Sensing and Chemometrics, College of Biology, Aptamer Engineering Center of Hunan Province, Hunan University, Changsha, Hunan, 410082, China.

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