SIMCA, analysis using

FIGURE 8.7. (a) 3D plot after SIMCA analysis using mean-centered preprocessing with five factors and a probability threshold of 0.95 for the opium samples from different locations, (b) 3D plot after SIMCA analysis using range scale preprocessing with three factors at a probability threshold of 0.95 for the poppy straw samples. See color insert. 

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 analysis provided highly variable results, while the quantile-BEAST gave better results overall and more consistent prediction. The best results were obtained when the quantile-BEAST algorithm used the full spectra, with no principal axis transformation. 

Despite the success of these techniques with other spectroscopic data, very little has been published on their use with Raman data. The aforementioned work on postconsumer plastic identification by Allen et al. [43] utilized KNN for their analysis, although they present little of the actual classification results. Similarly, Krizova et al. [54] simply state that the SIMCA analysis of Norway spruce needles resulted in similar results to PCA and cluster analysis studies. More detail was given by Daniel et al. [52] when comparing KNN and ANN for analysis of exposive materials. 

In this framework, the analysis of the volatile fraction was performed using HS-SPME [67] coupled with GC-MS system for the extraction and chromatographic separation and the identification of volatile organic compounds. The obtained GC-MS signals (Total Ion Current) were processed by SIMCA analysis. 

We also make a distinction between parametric and non-parametric techniques. In the parametric techniques such as linear discriminant analysis, UNEQ and SIMCA, statistical parameters of the distribution of the objects are used in the derivation of the decision function (almost always a multivariate normal distribution... 

In contrast, SIMCA uses principal components analysis to model object classes in the reduced number of dimensions. It calculates multidimensional boxes of varying size and shape to represent the class categories. Unknown samples are classified according to their Euclidean space proximity to the nearest multidimensional box. Kansiz et al. used both KNN and SIMCA for classification of cyanobacteria based on Fourier transform infrared spectroscopy (FTIR).44... 

Use of multivariate approaches based on classification modelling based on cluster analysis, factor analysis and the SIMCA technique [98,99], and the Kohonen artificial neural network [100]. All these methods, though rarely implemented, lead to very good results not achievable with classical strategies (comparisons, amino acid ratios, flow charts) and, moreover it is possible to know the confidence level of the classification carried out. 

A principal components multivariate statistical approach (SIMCA) was evaluated and applied to interpretation of isomer specific analysis of polychlorinated biphenyls (PCBs) using both a microcomputer and a main frame computer. Capillary column gas chromatography was employed for separation and detection of 69 individual PCB isomers. Computer programs were written in AMSII MUMPS to provide a laboratory data base for data manipulation. This data base greatly assisted the analysts in calculating isomer concentrations and data management. Applications of SIMCA for quality control, classification, and estimation of the composition of multi-Aroclor mixtures are described for characterization and study of complex environmental residues. 

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. 

The Fortran version used in this study was located at the Computer Center at the University of Illinois at Champaign/Urbana. The Fortran version is useful for analysis of very large data sets, i.e. 400 x 70 matrices. The SIMCA-3B version for microcomputer systems is interactive, menu driven, and is applicable to intermediate sized data sets and runs under CPM or MS-DOS. In this study, the SIMCA-3B program—CPLS-2, was used to obtain the results in the PLS examples discussed. 

The SIMCA approach can be applied in all of the four levels of pattern recognition. We focus on its use to describe complex mixtures graphically, and on its utility in quality control. This approach was selected for the tasks of developing a quality control program and evaluating similarities in samples of various types. Principal components analysis has proven to be well suited for evaluating data from capillary gas chromatographic (GC) analyses (6-8). 

A method successfully used for chromatographic data and capable to answer this and related questions is the SIMCA method (Statistical Isolinear Multiple Component Analysis). It has been constructed and developed by Svante Wold and his group at the University of Umea, Sweden. 

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. 

From the PCA analysis, it was concluded that appropriate ranks for the TEA and MEK SI.MCA models are two and one, respectively. The next step is to construct SIMCA models and test their performance on validation samples. The ranks determined during the PCA analyses and the default settings for the class volume size for the models are used. 

While KliN only uses physical closeness of samples to construct models, SIMCA uses the position and shape of the object formed by the samples in row space fordass definition. Modeling the object fonned by an individual class is accomplisfed with principal components analysis (PCA) (see Section 4.2.2). A multidimensional box is constructed for each class and the classification of fit ture samples is performed by determining within which box, if any, the sample belong (using an F test). 

Principal Component (PC) In this book, the tenn principal component is used as a generic term to indicate a factor or dimension when using SIMCA, principal components analysis, or principal components regression. Using this terminology, there are scores and loadings associated with a given PC. (See also Factor.)... 

Nevertheless, in most of the electronic tongue applications found in the literature, classification techniques like linear discriminant analysis (LDA) and partial least squares discriminant analysis (PLS-DA) have been used in place of more appropriate class-modeling methods. Moreover, in the few cases in which a class-modeling technique such as soft independent modeling of class analogy (SIMCA) is applied, attention is frequently focused only on its classification performance (e.g., correct classification rate). Use of such a restricted focus considerably underutilizes the significant characteristics of the class-modeling approach. 

The rule of the K nearest objects, KNN, has been used in classification problems, in connection and comparison with other methods. Usually KNN requires a preliminary standardization and, when the number of objects is large, the computing time becomes very long. So, it appears to be useful in confirmatory/exploratory analysis (to give information about the environment of objects) or when other classification methods fail. This can happen when the distribution of objects is very far from linear, so that the space of one category can penetrate into that of another, as in the two-dimensional example shown in Fig. 28, where the category spares, computed by bayesian analysis or SIMCA, widely overlap. 

Current methods for supervised pattern recognition are numerous. Typical linear methods are linear discriminant analysis (LDA) based on distance calculation, soft independent modeling of class analogy (SIMCA), which emphasizes similarities within a class, and PLS discriminant analysis (PLS-DA), which performs regression between spectra and class memberships. More advanced methods are based on nonlinear techniques, such as neural networks. Parametric versus nonparametric computations is a further distinction. In parametric techniques such as LDA, statistical parameters of normal sample distribution are used in the decision rules. Such restrictions do not influence nonparametric methods such as SIMCA, which perform more efficiently on NIR data collections. 

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