Principal Component Analysis for Nonlinear Optical Microscopic Chemical Imaging of Nitrogen Gas

TL;DR

This paper applies principal component analysis to enhance nonlinear optical microscopic chemical imaging of nitrogen gas, enabling rapid, noise-reduced visualization of microscopic gas flows using coherent Raman spectroscopy.

Contribution

The study introduces a PCA-based method to improve signal extraction and quantification in wide-field coherent Raman imaging of nitrogen gas, overcoming challenges of weak signals and background noise.

Findings

Nitrogen gas can be rapidly monitored within microvolumes in less than 0.2 seconds.

The PCA method effectively reduces background noise and normalizes intensity fluctuations.

Enhanced imaging allows visualization of microscopic gas flows otherwise invisible to infrared techniques.

Abstract

We have implemented principal component analysis for microscopic wide-field chemical imaging via coherent Raman spectroscopy. Microscopic imaging of nitrogen gas has been challenging due to extremely weak signals stemming from low order Raman interaction. Wide-field coherent Raman micro-spectroscopy has demonstrated the ability to chemically distinguish nitrogen gas although it has been difficult to quantify spatial-density information due to significant levels of background noise. By subtracting the Gaussian beam shape and removing contributions from uninformative noise simultaneously from the set of images, we can reconstruct the normalized intensity fluctuations. Our analysis demonstrates that nitrogen gas within microvolume can be rapidly monitored under ambient conditions in less than 0.2 seconds. We believe that our work has the potential to improve visualization of microscopic flows due to molecular dynamics of gases and/or liquids otherwise invisible to infrared optical techniques.