A novel multivariate curve resolution-alternating least squares (MCR-ALS) methodology for application in hyperspectral Raman imaging analysis-Reference-Cited by-同舟云学术

A novel multivariate curve resolution-alternating least squares (MCR-ALS) methodology for application in hyperspectral Raman imaging analysis

Published:2019 Issue:18 Volume:144 Page:5425-5438
ISSN:0003-2654
Container-title:The Analyst
language:en
Short-container-title:Analyst

Author:

Smith Joseph P.¹²³⁴⁵^ORCID,Holahan Erin C.⁶⁷⁸⁹,Smith Frank C.¹⁰⁷⁸⁹^ORCID,Marrero Veronica⁶⁷⁸⁹,Booksh Karl S.⁶⁷⁸⁹^ORCID

Affiliation:

1. Analytical Research & Development

2. Merck Research Laboratories

3. Merck & Co.

4. Inc.

5. Rahway

6. Department of Chemistry & Biochemistry

7. University of Delaware

8. Newark

9. USA

10. Department of Geological Sciences

Abstract

A new multivariate curve resolution-alternating least squares (MCR-ALS) methodology is presented that uses approximate reference spectra to determine optimal model complexity for identifying chemical constituents within hyperspectral imaging data.

Funder

National Aeronautics and Space Administration

National Science Foundation

Merck

University of Delaware

Publisher

Royal Society of Chemistry (RSC)

Subject

Electrochemistry,Spectroscopy,Environmental Chemistry,Biochemistry,Analytical Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2019/AN/C9AN00787C

Reference51 articles.

1. In-Process Control Assay of Pharmaceutical Microtablets Using Hyperspectral Imaging Coupled with Multivariate Analysis

2. Monitoring polymorphic transformations by using in situ Raman hyperspectral imaging and image multiset analysis

3. Distribution of a low dose compound within pharmaceutical tablet by using multivariate curve resolution on Raman hyperspectral images

4. Multivariate Curve Resolution Applied to Hyperspectral Imaging Analysis of Chocolate Samples

5. Combining multiset resolution and segmentation for hyperspectral image analysis of biological tissues

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