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Evaluating Limits Of Machine Learning-assisted Raman Spectroscopy In Classification Of Biological Samples.

Home
Evaluating Limits Of Machine Learning-assisted Raman Spectroscopy In Classification Of Biological Samples.

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

Aman Yadav¹, Arlin Birkby¹, Noah Armstrong¹

¹Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843, USA.

Biorxiv : the Preprint Server for Biology

|March 27, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

Machine learning (ML)-assisted Raman spectroscopy accuracy depends on data quality and sample similarity, not ML models. Optimizing experimental factors is key for reliable analyte classification.

Keywords:

Lipid analysis Machine Learning Raman Spectroscopy Saccharomyces cerevisiae Single cell analysis

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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.