Enhanced Sensitivity for Quantifying Disease Markers via Raman and Machine-Learning of Circulating Biofluids in Optofluidic Chips

TL;DR

This paper introduces a portable, automated Raman spectroscopy system combined with machine learning for highly sensitive, label-free detection of disease markers in biofluids, surpassing previous methods especially for analytes like glucose.

Contribution

The work presents a novel optofluidic Raman setup with a circulation approach and machine learning, enabling highly sensitive, user-independent quantification of disease markers in biofluids.

Findings

Achieved sub-mM accuracy in biofluid analysis.

Set a new record for label-free glucose measurement in blood.

Demonstrated robustness across different biofluid turbidity levels.

Abstract

We demonstrate novel instrumentation for spontaneous Raman spectroscopy in biofluids, enabling development of a portable, automated, reliable diagnostics technique requiring minimal operator expertise to quantify disease markers. Label-free Raman analysis of biofluids at physiologically-relevant sensitivities is achieved using a microfluidic-embedded liquid-core-waveguide augmented with a unique circulation approach: thermal damage and spectrum variance is minimized, eliminating conventional limits on integration time for excellent signal-to-noise ratio and temporal stability. Machine-learning then optimizes spectrum processing, yielding quantitative results independent of end-user proficiency. Sub-mM accuracy is achieved in solutions of both high and low turbidity, surpassing the sensitivity of previous techniques for analytes with a small scattering cross-section, such as glucose. We attain a new record for label-free glucose measurements in an artificial whole-blood, achieving an accuracy up to 0.14 mM, well-exceeding the 0.78 mM accuracy required for diabetic monitoring, establishing our technique's potential to significantly facilitate portable Raman for complex biofluid analysis.