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Whole-body Mass Spectrometry Imaging by Infrared Matrix-assisted Laser Desorption Electrospray Ionization (IR-MALDESI)

Whole-body Mass Spectrometry Imaging by Infrared Matrix-assisted Laser Desorption Electrospray Ionization (IR-MALDESI)

Published on: March 24, 2016

Infrared Mueller matrix acquisition and preprocessing system.

Arthur H Carrieri¹, David J Owens, Jonathan C Schultz

¹U.S. Army Research, Development, and Engineering Command, Edgewood Chemical Biological Center, Research and Technology Directorate, ATTN: AMSRD-ECB-RT-DL, 5183 Blackhawk Road, Aberdeen Proving Ground, Maryland 21010-5424, USA. arthur.carrieri@us.army.mil

|September 23, 2008

Summary

This summary is machine-generated.

A new system (AMMS) enables standoff detection of chemical-biological agents using infrared spectroscopy. It analyzes Mueller matrix data with a neural network for accurate identification.

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Area of Science:

Optics and Photonics
Spectroscopy
Chemical Sensing

Background:

Photopolarimetric sensors are crucial for standoff detection of chemical-biological agents.
The mid-infrared (9.1-12.0 microm) spectral band offers unique "fingerprint" signatures for analytes.
Existing systems require efficient Mueller matrix acquisition and preprocessing for reliable analysis.

Purpose of the Study:

To develop an analog Mueller matrix acquisition and preprocessing system (AMMS) for enhanced photopolarimetric sensing.
To enable standoff detection of chemical-biological analytes in the mid-infrared spectral region.
To integrate Mueller matrix data processing with neural network pattern recognition for analyte classification.

Main Methods:

Development of an analog Mueller matrix acquisition and preprocessing system (AMMS).
Utilizing a photopolarimetric sensor with a 9.1-12.0 microm optical bandwidth.
Employing two alternate polarization-modulated CO(2) laser beams for analyte excitation.
Acquiring and preprocessing Mueller matrix elements, including normalization, subtraction, and filtering.
Formatting data into digitized identification metrics for neural network analysis.

Main Results:

The AMMS system successfully acquires and preprocesses Mueller matrix data.
The system primes the photoelastic-modulation engine and optimizes throughput radiance.
Digitized identification metrics are generated from the preprocessed Mueller matrix data.
Pattern recognition and type classification of interrogated analytes are achieved via a neural network.

Conclusions:

The developed AMMS is effective for photopolarimetric-based standoff detection.
The system facilitates reliable chemical-biological agent identification using mid-infrared spectroscopy.
Integration with neural networks enhances the performance of Mueller matrix analysis for analyte classification.