Evaluating Limits of Machine Learning-Assisted Raman Spectroscopy in Classification of Biological Samples

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Machine Learning

Background:

• Machine learning (ML) enhances Raman spectroscopy for analyte classification.
• Technical challenges affecting ML-assisted Raman spectroscopy accuracy are underexplored.

Purpose of the Study:

• Investigate experimental factors impacting ML-assisted Raman spectroscopy classification performance.
• Identify key limitations for robust analyte detection.

Main Methods:

• Evaluated ML model performance and experimental factors (noise, spectral similarity).
• Analyzed single-cell Raman spectra from yeast strains with varying gene mutations.
• Assessed transfer learning effectiveness across different Raman spectrometers.

Main Results:

• Data quality and spectral similarity significantly impact classification accuracy, outweighing ML model effects.
• High spectral noise and similarity reduce accuracy; low noise enables discrimination of 1.85 mol% lipid differences.
• Cell-to-cell variability in yeast strains reduced single-cell classification accuracy; averaging spectra improved it.
• Transfer learning effectiveness depends on instrument calibration and standardization.

Conclusions:

• Data quality and spectral similarity are primary bottlenecks in ML-assisted Raman spectroscopy.
• Optimizing sample preparation, data acquisition, and instrument calibration is crucial for reliable performance.
• Addressing experimental factors is essential for advancing ML applications in spectroscopy.