PLS Toolbox

Figure 11.19 shows the results obtained after applying MCR-ALS with nonnegativity constraints to the same SVOC data set shown in Figure 11.16. Using five components, the explained data variance was 84.1%, very close to the value obtained by PCA (84.4%) for mean-centered data. MCR-ALS was directly applied to raw data, without any further data pretreatment apart from imputation of missing data (PLS Toolbox missing.m function) and setting values below the detection limit to...

The PLS Toolbox (Eigenvector Research, Inc, Wenatchee, WA, USA) also covers a large range of both two- and three-way multivariate methods including MCR. 

Wise B, Gallagher N, PLS.Toolbox 2.0, Eigenvector Research, Manson, WA, 1998. 

B.M. Wise, PLS Toolbox for Use with Matlab, version 1.4 (Eigenvector Technologies, West Richland, WA, USA). 

For the applications in this chapter the MATLAB gaselstr.m routine from the PLS Toolbox (from Eigenvector Research. 830 Wapato Lake Road, Manson, WA 98831, USA) is used. 

The limitations of loading plots and the application of MEDA were introduced with the pipelines artificial data set. This is further illustrated in this section with two examples provided with the PLS-toolbox (32) the Wine data set, which is used in the documentation of the cited software to show the capability of PCA for improving data understanding, and the... 

Fig. 6. MEDA matrices of the first PCs from the Wine data set provided with the PLS-toolbox (32).

Fig. 15. oMEDA vectors of the first 2 PCs from the Wine Data set provided with the PLS-toolbox (32). In (a), France and Czech Republic are compared. In (b), the trend shown in the score plot by all countries except Russia is analyzed. 

These instructions assume the use of MATLAB v. 7.0 (Math-Works Inc., Natick, MA, USA) along with PLS Toolbox V. 3.5 (Eigenvector Research, Manson, WA, USA) as the software platform for PCA analysis. The table of ToF-SIMS spectral data is imported into PLS Toolbox. Identified contamination peaks are excluded from the PCA data reduction. 

In a principal component model, each principal component is a linear combination of the original process variables defined in the data set. For a process with 10 process variables (file pv.mat), a principal components analysis was made using the PLS toolbox (Eigen vector research, 2004) and the result is shown in Table 21.1 ... 

The PLS toolbox by Eigenvector Research (2004) is a veiy useful tool to perform a principal component analysis. When the toolbox is installed under Matlab, it can be called by typing pea. The following graphical user interface appears ... 

The Eigenvector Research PCA/PLS toolbox (2004) is used to create a data set object named demoG. This file was created using CreateDemoG.m and can be loaded by typing >load de-moG. A principal component analysis can be performed by typing >pca. 

For the principal components analysis (PCA) and partial least squares regression (PLS) in Chapters 22 and 23, this book makes use of a PLS Toolbox, which is a product of Eigenvector Research, Inc. The PLS Toolbox is a collection of essential and advanced chemometric routines that work within the MATLAB conmutational environment. We are grateful to Eigenvector for permission. For Eigenvector product information please contact ... 

It is once again important to stress that this segmentation does not lead to a reduction of data, like binning does, but it provides an overall picture of the relevant information in different spectral subdivisions, focusing on important spectral regions [7]. /PLS models shown in this section were carried out in Matlab using the open source /PLS toolbox available at http //www.models. life.ku.dk/algorithms. 

---