Chemical mixture estimation under a Poisson Raman spectroscopy model

Ryan D. Palkki; Aaron D. Lanterman

doi:10.1117/1.3506203

1 November 2010 Chemical mixture estimation under a Poisson Raman spectroscopy model

Ryan D. Palkki, Aaron D. Lanterman

Author Affiliations +

Optical Engineering, Vol. 49, Issue 11, 113601 (November 2010). https://doi.org/10.1117/1.3506203

Abstract

Many methods have previously been devised to estimate the relative amounts of chemicals present in a measured Raman spectrum. However, relatively little work has been done on developing physics-based probabilistic models for the measurement system. Drawing from previous work in astronomical image restoration, we model the acquired data based on the physics of two key components in our Raman instrumentation: the spectrometer and the charge-coupled device detector. Under this model, we derive Cramér-Rao lower bounds for the mixing coefficients of the target spectra. This bound is compared against the performance of several classification algorithms. The non-negative iteratively reweighted least-squares algorithm is seen to give performance that is nearly identical to the more computationally demanding expectation-maximization approach; this is true even at weak signal levels.

©(2010) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Ryan D. Palkki and Aaron D. Lanterman "Chemical mixture estimation under a Poisson Raman spectroscopy model," Optical Engineering 49(11), 113601 (1 November 2010). https://doi.org/10.1117/1.3506203

Published: 1 November 2010

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available