Algorithm-assisted interpretation of cyclic and differential pulse voltammetry for cardiac troponin detection

Area of Science:

• Electrochemistry
• Biosensors
• Biomarker Detection

Background:

• Cardiovascular disease is a major global health concern requiring early detection.
• Electrochemical voltammetry (cyclic voltammetry and differential pulse voltammetry) is crucial for cardiac troponin detection.
• Interpreting raw voltammetric data is challenging due to noise, baseline drift, and manual analysis.

Purpose of the Study:

• To develop an algorithm-assisted framework for automated interpretation of voltammetric data.
• To enhance the accuracy and reproducibility of electrochemical biosensor measurements for cardiac biomarkers.
• To support early screening of cardiovascular disease.

Main Methods:

• Utilized polynomial fitting for cyclic voltammetry (CV) baseline correction.
• Employed asymmetric least squares (ALS) for differential pulse voltammetry (DPV) baseline correction.
• Extracted peak-to-baseline current response as a quantitative biomarker indicator.

Main Results:

• The framework successfully identified characteristic voltammetric peaks.
• Distinguished samples with high and low cardiac biomarker levels relative to a detection threshold.
• Demonstrated close agreement with reference electrochemical analysis software for peak detection and baseline estimation.

Conclusions:

• The algorithm-assisted framework automates voltammetric data interpretation, reducing manual analysis.
• Improved signal clarity and reduced operator dependency enhance reproducibility and accessibility.
• The method supports reliable and efficient early screening of cardiac biomarkers.