Partial Least-Squares Quantitative Analysis of Infrared Spectroscopic Data. Part I: Algorithm Implementation
Applied Spectroscopy, Vol. 42, Issue 2, pp. 217-227 (1988)
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Abstract
Various approaches to infrared multicomponent quantitative analysis including K-matrix, multivariate least-squares, principal component regression (PCR), and partial least-squares (PLS) are compared. The advantages and disadvantages of each are discussed. A particular implementation of the PLS method is detailed, with emphasis on the methods provided for calibration optimization and evaluation.
Citation
M. P. Fuller, G. L. Ritter, and C. S. Draper, "Partial Least-Squares Quantitative Analysis of Infrared Spectroscopic Data. Part I: Algorithm Implementation," Appl. Spectrosc. 42, 217-227 (1988)
http://www.opticsinfobase.org/as/abstract.cfm?URI=as-42-2-217
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