Machine Learning-Assisted Raman Spectroscopy for pH and Lactate Sensing in Body Fluids

Area of Science:

• Biomedical Engineering
• Analytical Chemistry
• Spectroscopy

Background:

• pH and lactate levels are critical biomarkers for various pathologies.
• Current monitoring methods can be invasive or lack continuous real-time data.
• Raman spectroscopy offers a non-destructive analytical technique.

Purpose of the Study:

• To develop and validate a diagnostic tool using Raman spectroscopy and machine learning for pH and lactate detection.
• To assess the method's applicability in vitro and ex vivo.
• To establish a foundation for noninvasive, continuous in vivo monitoring.

Main Methods:

• Raman spectroscopy was used to analyze aqueous solutions, blood, and plasma samples.
• Machine learning algorithms, including Principal Component Analysis (PCA), were employed for spectral feature extraction.
• Partial Least Squares Regression (PLSR) models were developed to quantify pH and lactate concentrations.

Main Results:

• Characteristic spectral patterns for varying pH and lactate concentrations were identified.
• PCA effectively highlighted spectral features differentiating samples based on pH and lactate.
• PLSR models achieved clinically accurate predictions of pH and lactate in unknown samples.

Conclusions:

• The combination of Raman spectroscopy and machine learning is a promising approach for detecting and monitoring pH and lactate.
• The validated method demonstrates potential for developing noninvasive, continuous monitoring technologies.
• This technique could significantly advance the management of conditions related to pH and lactate imbalances.