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A conventional Raman spectrophotometer includes a laser source, a sample holding system, a wavelength selector, and a detector.
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Automated Raman Spectral Preprocessing of Bone and Other Musculoskeletal Tissues.

Francis W L Esmonde-White¹, Matthew V Schulmerich¹, Karen A Esmonde-White²

¹Dept. of Chemistry, Univ. of Michigan, 930 N. University, Ann Arbor, MI, USA 48109-1055.

Proceedings of Spie--The International Society for Optical Engineering

|September 18, 2020

Summary

This study optimizes Raman spectroscopy for bone analysis by automating calibration and preprocessing. These methods improve accuracy and reduce spectral noise for better bone tissue studies.

Keywords:

Raman Spectroscopy background correction bone dichroic filter spectra fluorescence removal image rotation musculoskeletal tissue preprocessing spike removal

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Area of Science:

Biomedical Engineering
Spectroscopy
Materials Science

Background:

Raman spectroscopy of bone is challenging due to fluorescence and overlapping tissue signals.
Accurate calibration and preprocessing are crucial for effective bone analysis using Raman spectroscopy.

Purpose of the Study:

To optimize and automate calibration, preprocessing, and background correction methods for bone Raman spectroscopy.
To enhance the accuracy and efficiency of Raman spectroscopy in bone studies.

Main Methods:

Developed a step-wise approach to automate spectral data processing.
Improved manual spike removal, white light correction, image rotation, and slit curvature correction algorithms.
Focused on minimizing mathematical complexity for broader applicability.

Main Results:

Successfully optimized automated calibration and preprocessing techniques for bone Raman spectroscopy.
Demonstrated improvements in spectral accuracy by addressing background fluorescence and tissue interference.
Provided a streamlined workflow for bone spectral analysis.

Conclusions:

The developed automated methods significantly enhance the utility of Raman spectroscopy for bone research.
Accurate and rapid preprocessing is essential for reliable bone tissue characterization.
This work facilitates wider adoption of Raman spectroscopy in skeletal studies.