Machine Learning-Assisted Portable Dual-Readout Biosensor for Visual Detection of Milk Allergen.

Journal: Nano letters
PMID: 40111970

Abstract

Beta-lactoglobulin (β-LG), the primary allergen in cow's milk, makes developing a rapid, sensitive, and convenient detection method essential for individuals with allergies. In this study, a graphdiyne-based self-powered electrochemical biosensor has been cleverly integrated into the corresponding test strip. This biosensor uses glucose as fuel and correlates the β-LG concentration with the glucose value displayed on a mobile phone application, enabling real-time and quantitative detection. Additionally, an electrochromic substance reacts with the byproduct (HO) of glucose oxidation by biological enzymes. A quantitative relationship between color and β-LG concentration has been established using mobile phone software. Dual detection of electrochemical and colorimetric signals in the 0.01-10,000 ng/mL range, with detection limits as low as 0.0033 and 0.0081 ng/mL, is possible. Machine learning is finally employed to analyze its performance. Our dual-readout biosensor demonstrates significant potential for rapid food allergy detection.

Authors

  • Jing Xu
    First Department of Infectious Diseases, The First Affiliated Hospital of China Medical University, Shenyang, China.
  • Xinqi Luo
    Department of Urology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710004, China.
  • Mohan Duan
    College of Chemistry and Chemical Engineering, Xinyang Normal University, Xinyang 464000, China.
  • Kexin Guo
    College of Chemistry and Chemical Engineering, Xinyang Normal University, Xinyang 464000, China.
  • Yuxiao Shangguan
    College of Chemistry and Chemical Engineering, Xinyang Normal University, Xinyang 464000, China.
  • Qingle Zhao
    College of Chemistry and Chemical Engineering, Xinyang Normal University, Xinyang 464000, China.
  • Minyu Qiu
    Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong 999077, China.
  • Fu Wang
    Department of Computer Science and Engineering, Shanghai Jiao Tong University, Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai, China.

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