By Topic

A hybrid least squares and principal component analysis algorithm for Raman spectroscopy

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Dominique Van de Sompel ; Molecular Imaging Program at Stanford (MIPS), Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA ; Ellis Garai ; Cristina Zavaleta ; Sanjiv Sam Gambhir

The least squares fitting algorithm is the most commonly used algorithm in Raman spectroscopy. In this paper, however, we show that it is sensitive to variations in the background signal when the signal of interest is weak. To address this problem, we propose a novel algorithm to analyze measured spectra in Raman spectroscopy. The method is a hybrid least squares and principal component analysis algorithm. It explicitly accounts for any variations expected in the reference spectra used in the signal decomposition. We compare the novel algorithm to the least squares method with a low-order polynomial residual model, and demonstrate the novel algorithm's superior performance by comparing quantitative error metrics. Our experiments use both simulated data and data acquired from an in vitro solution of Raman-enhanced gold nanoparticles.

Published in:

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Date of Conference:

Aug. 30 2011-Sept. 3 2011