Exploration of new multivariate spectral calibration algorithms. Metadata

Metadata describes a digital item, providing (if known) such information as creator, publisher, contents, size, relationship to other resources, and more. Metadata may also contain "preservation" components that help us to maintain the integrity of digital files over time.

Title

  • Main Title Exploration of new multivariate spectral calibration algorithms.

Creator

  • Author: Van Benthem, Mark Hilary
    Creator Type: Personal
  • Author: Haaland, David Michael
    Creator Type: Personal
  • Author: Melgaard, David Kennett
    Creator Type: Personal
  • Author: Martin, Laura Elizabeth
    Creator Type: Personal
  • Author: Wehlburg, Christine Marie
    Creator Type: Personal
  • Author: Pell, Randy J. (The Dow Chemical Company, Midland, MI)
    Creator Type: Personal
  • Author: Guenard, Robert D. (Merck & Co. Inc., West Point, PA)
    Creator Type: Personal

Contributor

  • Sponsor: United States. Department of Energy.
    Contributor Type: Organization

Publisher

  • Name: Sandia National Laboratories
    Place of Publication: United States

Date

  • Creation: 2004-03-01

Language

  • English

Description

  • Content Description: A variety of multivariate calibration algorithms for quantitative spectral analyses were investigated and compared, and new algorithms were developed in the course of this Laboratory Directed Research and Development project. We were able to demonstrate the ability of the hybrid classical least squares/partial least squares (CLSIPLS) calibration algorithms to maintain calibrations in the presence of spectrometer drift and to transfer calibrations between spectrometers from the same or different manufacturers. These methods were found to be as good or better in prediction ability as the commonly used partial least squares (PLS) method. We also present the theory for an entirely new class of algorithms labeled augmented classical least squares (ACLS) methods. New factor selection methods are developed and described for the ACLS algorithms. These factor selection methods are demonstrated using near-infrared spectra collected from a system of dilute aqueous solutions. The ACLS algorithm is also shown to provide improved ease of use and better prediction ability than PLS when transferring calibrations between near-infrared calibrations from the same manufacturer. Finally, simulations incorporating either ideal or realistic errors in the spectra were used to compare the prediction abilities of the new ACLS algorithm with that of PLS. We found that in the presence of realistic errors with non-uniform spectral error variance across spectral channels or with spectral errors correlated between frequency channels, ACLS methods generally out-performed the more commonly used PLS method. These results demonstrate the need for realistic error structure in simulations when the prediction abilities of various algorithms are compared. The combination of equal or superior prediction ability and the ease of use of the ACLS algorithms make the new ACLS methods the preferred algorithms to use for multivariate spectral calibrations.
  • Physical Description: 40 p.

Subject

  • Keyword: Calibration
  • Keyword: Spectrometers
  • Keyword: Error Analysis (Mathematics)
  • STI Subject Categories: 99 General And Miscellaneous//Mathematics, Computing, And Information Science
  • Keyword: Multivariate Analysis
  • Keyword: Multivariate Analysis.
  • Keyword: Calibration.
  • Keyword: Algorithms.
  • Keyword: Algorithms
  • Keyword: Least Square Fit
  • Keyword: Infrared Spectra Error Analysis (Mathematics)
  • Keyword: Simulation Methods.
  • Keyword: Spectral Analysis-Instruments
  • STI Subject Categories: 97

Collection

  • Name: Office of Scientific & Technical Information Technical Reports
    Code: OSTI

Institution

  • Name: UNT Libraries Government Documents Department
    Code: UNTGD

Resource Type

  • Report

Format

  • Text

Identifier

  • Report No.: SAND2004-1053
  • Grant Number: AC04-94AL85000
  • DOI: 10.2172/918755
  • Office of Scientific & Technical Information Report Number: 918755
  • Archival Resource Key: ark:/67531/metadc883329