Machine learning-enhanced drug testing for simultaneous morphine and methadone detection in urinary biofluids.

Area of Science:

• Electrochemistry
• Materials Science
• Analytical Chemistry

Background:

• Simultaneous drug identification is challenging due to complex biological matrices and analyte interactions.
• Existing methods often struggle with precision and sensitivity in real-world samples.

Purpose of the Study:

• To develop an innovative electrochemical sensor for the precise and simultaneous determination of morphine (MOR), methadone (MET), and uric acid (UA) in urine.
• To overcome the limitations of current analytical techniques in complex biological samples.

Main Methods:

• Fabrication of a novel electrochemical sensor using carbon nanotubes (CNT) modified with graphitic carbon nitride (g-C3N4) nanosheets.
• Utilized fast Fourier transform (FFT) voltammetry for quantitative measurements.
• Employed partial least squares (PLS) machine learning for predictive modeling and validation.

Main Results:

• The sensor achieved high sensitivity and precision for simultaneous MOR, MET, and UA detection.
• Low RMSECV and RMSEP values demonstrated the model's accuracy (e.g., MOR RMSECV: 0.1827 µM, RMSEP: 0.1925 µM).
• Excellent performance in real urine samples with low RSD (3.71-5.26%) and high recovery (96-106%).

Conclusions:

• The developed g-C3N4-CNT electrochemical sensor offers a robust and reliable platform for simultaneous drug analysis in complex biological matrices.
• The integration of advanced materials and machine learning signifies a significant advancement in electrochemical sensing for clinical and practical applications.
• This technology holds promise for transforming drug analysis, enabling more accurate and efficient diagnostics.