Hyperspectral data processing for chemoselective multiplex coherent anti-Stokes Raman scattering microscopy of...

Area of Science:

• Nonlinear optical microscopy
• Chemical imaging
• Spectroscopy

Background:

• Multiplex coherent anti-Stokes Raman scattering (MCARS) enables label-free, fast characterization of materials and biological samples.
• Current MCARS data analysis for chemoselective imaging is limited by reliance on single spectral features, restricting analysis to strong, well-separated Raman resonances.
• This limitation hinders detailed chemical component analysis in complex samples like biological tissues.

Purpose of the Study:

• To develop and present a novel MCARS data processing method for enhanced chemoselective imaging.
• To overcome the limitations of traditional single-feature analysis in MCARS.
• To enable disentanglement of overlapping spectral contributions from different chemical components without prior spectral knowledge.

Main Methods:

• A new 'two-step' MCARS data processing approach was implemented.
• The method combines imaginary part extraction with global fitting of the hyperspectral dataset.
• This approach utilizes the entire measured spectrum, eliminating the need for prior knowledge of pure component spectra.

Main Results:

• The developed method successfully disentangles overlapping spectral contributions from various chemical components.
• It generates highly contrasted images where different sample components are represented in distinct colors.
• The method was successfully applied to complex polymer samples and biological tissues, demonstrating its versatility.

Conclusions:

• The novel two-step MCARS data processing method significantly improves chemoselective imaging capabilities.
• This approach overcomes previous limitations, enabling detailed analysis of complex chemical compositions in various samples.
• The technique offers a powerful tool for advanced material and biological sample characterization in nonlinear microscopy.