Pretreatments by means of orthogonal projections

被引:36
作者
Boulet, Jean-Claude [1 ]
Roger, Jean-Michel [2 ]
机构
[1] INRA, Sci Oenol UMR1083, F-34060 Montpellier, France
[2] IRSTEA, UMR ITAP Informat Technol Anal Environm Procedes, F-34196 Montpellier, France
关键词
Orthogonal projection; Pretreatment; Preprocessing; Subspace; Linear model; NEAR-INFRARED SPECTRA; ANALYTE SIGNAL CALCULATION; BASE-LINE CORRECTION; MULTIVARIATE CALIBRATION; PREPROCESSING METHODS; REFLECTANCE SPECTRA; WAVELET TRANSFORMS; PLS; REGRESSION; CLASSIFICATION;
D O I
10.1016/j.chemolab.2012.02.002
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This article describes several linear pretreatments based on orthogonal projections. The main differences of these pretreatments lie in the way the information to be removed are identified, using calibration dataset, pure spectra, experimental designs or mathematical models. Removing all the undesired spectral information yields spectra proportional to the net analyte signal, so it is important to collect the most complete information possible, using the complementarities of different approaches. The correction should then be processed with a single Euclidian orthogonal projection that gathers all the information, rather than with successive operations. By embedding Euclidian orthogonal projections into the calibration, it is not necessary to reapply them to new datasets. (C) 2012 Elsevier B.V. All rights reserved.
引用
收藏
页码:61 / 69
页数:9
相关论文
共 43 条
[1]   An introduction to wavelet transforms for chemometricians: A time-frequency approach [J].
Alsberg, BK ;
Woodward, AM ;
Kell, DB .
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 1997, 37 (02) :215-239
[2]   Direct orthogonalization [J].
Andersson, CA .
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 1999, 47 (01) :51-63
[3]   Transfer by orthogonal projection: making near-infrared calibrations robust to between-instrument variation [J].
Andrew, A ;
Fearn, T .
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2004, 72 (01) :51-56
[4]   Application of wavelet transforms to experimental spectra: Smoothing, denoising, and data set compression [J].
Barclay, VJ ;
Bonner, RF ;
Hamilton, IP .
ANALYTICAL CHEMISTRY, 1997, 69 (01) :78-90
[5]  
Barnes R.J., 1993, J NEAR INFRARED SPEC, V1, P185, DOI DOI 10.1255/JNIRS.21
[6]   STANDARD NORMAL VARIATE TRANSFORMATION AND DE-TRENDING OF NEAR-INFRARED DIFFUSE REFLECTANCE SPECTRA [J].
BARNES, RJ ;
DHANOA, MS ;
LISTER, SJ .
APPLIED SPECTROSCOPY, 1989, 43 (05) :772-777
[7]   Improvement of calibration models using two successive orthogonal projection methods. Application to quantification of wine mannoproteins [J].
Boulet, J. C. ;
Doco, T. ;
Roger, J. M. .
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2007, 87 (02) :295-302
[8]   Theory of net analyte signal vectors in inverse regression [J].
Bro, R ;
Andersen, CM .
JOURNAL OF CHEMOMETRICS, 2003, 17 (12) :646-652
[9]  
Brown J.M., 1992, US Patent, Patent No. [5121337 A, 5121337]
[10]  
Chang C.I., 2003, Hyperspectral Imaging: Techniques for Spectral Detection and Classification, V1