Hyperspectral Confocal Fluorescence Imaging: Exploring Alternative Multivariate Curve Resolution Approaches

$29.00 plus tax (Refund Policy)

Buy Article:

Abstract:

Hyperspectral confocal fluorescence microscopy, when combined with multivariate curve resolution (MCR), provides a powerful new tool for improved quantitative imaging of multi-fluorophore samples. Generally, fully non-negatively constrained models are used in the constrained alternating least squares MCR analyses of hyperspectral images since real emission components are expected to have non-negative pure emission spectra and concentrations. However, in this paper, we demonstrate four separate cases in which partially constrained models are preferred over the fully constrained MCR models. These partially constrained MCR models can sometimes be preferred when system artifacts are present in the data or where small perturbations of the major emission components are present due to environmental effects or small geometric changes in the fluorescing species. Here we demonstrate that in the cases of hyperspectral images obtained from multicomponent spherical beads, autofluorescence from fixed lung epithelial cells, fluorescence of quantum dots in aqueous solutions, and images of mercurochrome-stained endosperm portions of a wild-type corn seed, these alternative, partially constrained MCR analyses provide improved interpretability of the MCR solutions. Often the system artifacts or environmental effects are more readily described as first and/or second derivatives of the main emission components in these alternative MCR solutions since they indicate spectral shifts and/or spectral broadening or narrowing of the emission bands, respectively. Thus, this paper serves to demonstrate the need to test alternative partially constrained models when analyzing hyperspectral images with MCR methods.

Keywords: ALS; AUTOFLUORESCENCE; CORN ENDOSPERM; FLUORESCENCE IMAGING; HYPERSPECTRAL CONFOCAL MICROSCOPE; MCR; MULTIVARIATE CURVE RESOLUTION; PARTIALLY CONSTRAINED ALTERNATING LEAST SQUARES; QUANTUM DOTS

Document Type: Research Article

DOI: http://dx.doi.org/10.1366/000370209787598843

Affiliations: 1: Sandia National Laboratories, Albuquerque, New Mexico 87185-0895 2: Monsanto Company, St. Louis, Missouri 63167 3: Internal Medicine-Endocrinology, University of Texas Medical Branch, Galveston, Texas 77555-1060 4: Department of Pathology, Cancer Research and Treatment Center, University of New Mexico, Albuquerque, New Mexico 87131

Publication date: March 1, 2009

More about this publication?

Tools

Favourites

Share Content

Access Key

Free Content
Free content
New Content
New content
Open Access Content
Open access content
Subscribed Content
Subscribed content
Free Trial Content
Free trial content
Cookie Policy
X
Cookie Policy
ingentaconnect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more