An Augmented Classical Least Squares Method for Quantitative Raman Spectral Analysis against Component Information Loss
Joint Authors
Source
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-07-08
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
We propose an augmented classical least squares (ACLS) calibration method for quantitative Raman spectral analysis against component information loss.
The Raman spectral signals with low analyte concentration correlations were selected and used as the substitutes for unknown quantitative component information during the CLS calibration procedure.
The number of selected signals was determined by using the leave-one-out root-mean-square error of cross-validation (RMSECV) curve.
An ACLS model was built based on the augmented concentration matrix and the reference spectral signal matrix.
The proposed method was compared with partial least squares (PLS) and principal component regression (PCR) using one example: a data set recorded from an experiment of analyte concentration determination using Raman spectroscopy.
A 2-fold cross-validation with Venetian blinds strategy was exploited to evaluate the predictive power of the proposed method.
The one-way variance analysis (ANOVA) was used to access the predictive power difference between the proposed method and existing methods.
Results indicated that the proposed method is effective at increasing the robust predictive power of traditional CLS model against component information loss and its predictive power is comparable to that of PLS or PCR.
American Psychological Association (APA)
Zhou, Yan& Cao, Hui. 2013. An Augmented Classical Least Squares Method for Quantitative Raman Spectral Analysis against Component Information Loss. The Scientific World Journal،Vol. 2013, no. 2013, pp.1-6.
https://search.emarefa.net/detail/BIM-1011927
Modern Language Association (MLA)
Zhou, Yan& Cao, Hui. An Augmented Classical Least Squares Method for Quantitative Raman Spectral Analysis against Component Information Loss. The Scientific World Journal No. 2013 (2013), pp.1-6.
https://search.emarefa.net/detail/BIM-1011927
American Medical Association (AMA)
Zhou, Yan& Cao, Hui. An Augmented Classical Least Squares Method for Quantitative Raman Spectral Analysis against Component Information Loss. The Scientific World Journal. 2013. Vol. 2013, no. 2013, pp.1-6.
https://search.emarefa.net/detail/BIM-1011927
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1011927