SIMCA software

Using our dataset which includes all of the descriptors mentioned so far, we conducted a PLS analysis using SIMCA software [34], In the initial PLS model, MW, V, and a (Alpha) were removed because they are in each case highly correlated with CMR (r > 0.95). SIMCA s VIP function selected only qmin (Qnegmin) for removal on the basis of it making no important contribution to the model. In the second model, 2q+/a (SQpos A) and ECa/a (SCa A) coincided nearly exactly in the three-component space of these two, we decided to keep only ECa/a in the third and final model. This model consisted of three components and accounted for 75% of the variance in log SQ the Q2 value was 0.66. 

The SIMCA software is available in two forms, both developed by Wold (2 5, 31) 1) an interactive, Fortran version which runs on Control Data Corporation (CDC) machines, and 2) an interactive version, SIMCA-3B. Additional information on these programs is contained in Appendix I. Only the SIMCA-3B version contains the CPLS-2 program used for PLS analysis. 

This parameter is similar and closely related to the DModX, used in the SIMCA software [in fact, DModXaorm = (OR2X)]. 

At the same time S. Wold presented the software soft independent modeling of class analogies (SIMCA) and introduced a new way of thinking in data evaluation called soft modeling (Wold and Sjostrom 1977). 

Although the SIMCA method is very versatile, and a properly optimized model can be very effective, one must keep in mind that this method does not use, or even calculate, between-class variability. This can be problematic in special cases where there is strong natural clustering of samples that is not relevant to the problem. In such cases, the inherent interclass distance can be rather low compared to the mtraclass variation, thus rendering the classification problem very difficult. Furthermore, from a practical viewpoint, the SIMCA method requires that one must obtain sufficient calibration samples to fully represent each of the J classes. Also, the on-line deployment of a SIMCA model requires a fair amount of overhead, due to the relatively large number of parameters and somewhat complex data processing instructions required. However, there are several current software products that facilitate SIMCA deployment. 

It is important to be able to view the structure of the data for the classes. This is done in a variety of ways depending on the analytical methods. The graphical technique most commonly used is that of plotting eigenvectors or principal components. SIMCA uses this method and software has been developed for three-dimensional color display of principal components data. Other plotting techniques are also used in SIMCA. 

This multivariate data analysis challenge is aggressively being met by a number of researchers. The result is a vibrant and growing literature filled with software acronyms such as ARTHUR,. SIMCA, CHEOPS, CLEOPATRA,... 

When the PCA analysis is completed and the calibration and validation sets are chosen, the next step is to create SIMCA models for the calibration set samples. The initial rank estimates from PCA and software default class volumes are used, the performance of the models on the test samples is examined, and the SIMCA settings are adjusted as necessary. 

Once the class boundaries are defined, it is important to determine whether any of the classes in the training set overlap. This indicates the discriminating power of the SIMCA models and will impact the confidence that can be placed on future predictions. TTiere are various algorithmic measures of class overlap and the reader is referred to their software package documentation for details. In this chapter, class overlap is indicated when training set samples are predicted to be members of multiple classes. This is demonstrated in a two-dimensional example shown in Figure 4.59- Two classes are shown where class A is described by one principal component and class B is described by two principal components. The overlap of the classes is indicated because unknown Z is classified as belonging to both classes. 

One of die difficulties is to decide what software to employ in order to analyse the data. This book is not restrictive and you can use any approach you like. Some readers like to program their own mediods, for example in C or Visual Basic. Others may like to use a statistical packages such as SAS or SPSS. Some groups use ready packaged chemometrics software such as Pirouette, Simca, Unscrambler and several others on the market. One problem widi using packages is that they are often very... 

Von diesen Techniken ist wiederum die PCA allgemein am gebrauchlichsten, so daB sie an dieser Stelle kurz erlSutert werden soli (vgl. dazu Abb. 10-1). Fur ihre Durch-fiihrung sind verschiedene Software-Pakete, teilweise auch als PD-Software erhaltlich (Programmnamen u.a. SIMCA, Pirouette, Minitab Scan for Windows). 

Quantitative multivariate models provide the advantage of predicting the properties of new molecules, even before they are synthesized. This provides synthetic direction for improving pharmaceutical properties and helps to prioritize the synthetic efforts. Winiwarter et al. [33] used multivariate analysis to develop a model for predicting in vivo human jejunal permeability using experimentally and theoretically derived descriptors. Statistical software SIMCA from Umetrics AB (www.umetrics.com) was used. Its stepwise process for model building, which appears to be widely applicable, was as follows. 

PCA and related two-way multivariate techniques can be performed in several commercial chemometrics softwares, such as SIMCA-P/P + (Umet-rics AB, Umea, Sweden) and The Unscrambler (Camo Inc., Woodbridge, New Jersey, USA). Batch analysis can be performed in SIMCA-P+ only. 

Of the many MVA outputs that can be generated using the Simca-P software, three dissimilar ones were selected for comparison Variable Importance Plot to rank the X s in terms of importance to modelling the Y s R and values for each of the two Y s and Observed vs. Predicted to examine how well the PLS model can predict new Y s based on the X s. 

To this end, any commercially available software (e.g., SAS, NCSS, IBM SPSS Statistics, SIMCA-P) can be used to perform the analysis. It has been well recognized that use of a proper statistical analysis method is essential to improve visualization, accurate classification, and outlier estimation [27]. 

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