Novel Phosphoproteins Detection Method Developed Using Lateral Flow Immunoassays

Strategic Insights -

Research conducted by Changrui Xing and colleagues at the Nanjing University of Finance and Economics has led to the development of a novel method for detecting phosphoproteins using lateral flow immunoassays. The new method allows for the detection of bovine milk adulteration in goat and camel milk, achieving linear ranges of 1-25 μg/g with acceptable recovery rates. Funded by the National Natural Science Foundation of China (NSFC) and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), the study demonstrated the effectiveness of the method through computational models that accurately predict test line responses to varying casein concentrations and antigen-antibody complex formation.

Key Takeaways:

  • The novel method uses lateral flow immunoassays to detect phosphoproteins and achieve linear ranges of 1-25 μg/g with acceptable recovery rates.
  • The method was developed by Changrui Xing and colleagues at the Nanjing University of Finance and Economics and is funded by the National Natural Science Foundation of China (NSFC) and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).
  • The study demonstrated the use of computational models to describe the kinetics of single-line and multiline competitive LFAs, which accurately predict test line responses to varying casein concentrations and antigen-antibody complex formation.
  • The key novelty of the work is the observed 'reverse ladder' pattern, where sequential antigen-antibody signal changes across a wide range of sample concentrations at different test zones provide an intuitive visual semi-quantitative readout.
  • The study concludes that the model establishes a foundational framework for multiline LFA design, offering a generalizable approach to optimizing test line configurations for enhanced sensitivity and dynamic range.

Statistics:

  • The new method achieved linear ranges of 1-25 μg/g with acceptable recovery rates.
  • The computational models predicted test line responses to varying casein concentrations and antigen-antibody complex formation with high accuracy.
  • The study demonstrated the effectiveness of the method through the detection of bovine milk adulteration in goat and camel milk.
  • The method was found to produce a graded visual fingerprint, where reverse ladder patterns correlate with different adulteration levels under optimized conditions.

Sources:

  • Xing, C., et al. (2025) "Detection of Bovine Milk Adulteration In Goat and Camel Milk and Numerical Simulation of Competitive Multiline Lateral Flow System." Microchemical Journal, 218.
  • Elsevier. (2025). Microchemical Journal. Retrieved from