A comparison of a common approach to partial least squares-discriminant analysis and classical least squares in hyperspectral imaging

被引:35
作者
Amigo, Jose Manuel [1 ]
Ravn, Carsten [2 ,3 ]
Gallagher, Neal B. [4 ]
Bro, Rasmus [1 ]
机构
[1] Univ Copenhagen, Fac Life Sci, Dept Food Sci Qual & Technol, DK-1958 Frederiksberg C, Denmark
[2] Univ Copenhagen, Fac Pharmaceut Sci, DK-2100 Copenhagen O, Denmark
[3] Novo Nordisk AS, DK-2760 Malov, Denmark
[4] Eigenvector Res Inc, Manson, WA 98831 USA
关键词
Hyperspectral imaging; CLS; PLS-DA; PLS-Classification; PLS-Class; MULTIVARIATE CALIBRATION; INFORMATION; QUALITY;
D O I
10.1016/j.ijpharm.2009.02.014
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
In hyperspectral analysis, PLS-discriminant analysis (PLS-DA) is being increasingly used in conjunction with pure spectra where it is often referred to as PLS-Classification (PLS-Class). PLS-Class has been presented as a novel approach making it possible to obtain qualitative information about the distribution of the compounds in each pixel using little a priori knowledge about the image (only the pure spectrum of each compound is needed). In this short note it is shown that the PLS-Class model is the same as a straightforward classical least squares (CLS) model and it is highlighted that it is more appropriate to view this approach as CLS rather than PLS-DA. A real example illustrates the results of applying both PLS-Class and CLS. (C) 2009 Elsevier B.V. All rights reserved.
引用
收藏
页码:179 / 182
页数:4
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