Classification, multivariate

Other methods consist of algorithms based on multivariate classification techniques or neural networks they are constructed for automatic recognition of structural properties from spectral data, or for simulation of spectra from structural properties [83]. Multivariate data analysis for spectrum interpretation is based on the characterization of spectra by a set of spectral features. A spectrum can be considered as a point in a multidimensional space with the coordinates defined by spectral features. Exploratory data analysis and cluster analysis are used to investigate the multidimensional space and to evaluate rules to distinguish structure classes. 

Spectral features and their corresponding molecular descriptors are then applied to mathematical techniques of multivariate data analysis, such as principal component analysis (PCA) for exploratory data analysis or multivariate classification for the development of spectral classifiers [84-87]. Principal component analysis results in a scatter plot that exhibits spectra-structure relationships by clustering similarities in spectral and/or structural features [88, 89]. 

In multivariate classification, the latent variable is a discriminant variable possessing optimum capability to separate two object classes. 

Multivariate classification has the aim to assign objects correctly to given classes (for instance different origins of samples). One approach is to use a latent... 

A great variety of different methods for multivariate classification (pattern recognition) is available (Table 5.6). The conceptually most simply one is fc-NN classification (Section 5.3.3), which is solely based on the fundamental hypothesis of multivariate data analysis, that the distance between objects is related to the similarity of the objects. fc-NN does not assume any model of the object groups, is nonlinear, applicable to multicategory classification, and mathematically very simple furthermore, the method is very similar to spectral similarity search. On the other hand, an example for a rather sophisticated classification method is the SVM (Section 5.6). 

Currently, several linear and nonlinear multivariate classification methods exist the choice implies the evaluation of discriminatory power against the ability to interpret the meaning of class differences. In this respect, Soft Independent Modeling of Class Analogy (SIMCA ... 

Robust multivariate classification can require large numbers of samples in order to characterize the covariance of the component distributions. One of the major advantages of spectral imaging over single-point spectroscopy is the ready... 

CLAS Central Laboratory for Clinical Chemistry, University Hospital, P.O.Box 30001, NL-9700 RB Groningen, The Netherlands Dfl 1000. Multivariate classification methods (ref. 15). 

Thousands of chemical compounds have been identified in oils and fats, although only a few hundred are used in authentication. This means that each object (food sample) may have a unique position in an abstract n-dimensional hyperspace. A concept that is difficult to interpret by analysts as a data matrix exceeding three features already poses a problem. The art of extracting chemically relevant information from data produced in chemical experiments by means of statistical and mathematical tools is called chemometrics. It is an indirect approach to the study of the effects of multivariate factors (or variables) and hidden patterns in complex sets of data. Chemometrics is routinely used for (a) exploring patterns of association in data, and (b) preparing and using multivariate classification models. The arrival of chemometrics techniques has allowed the quantitative as well as qualitative analysis of multivariate data and, in consequence, it has allowed the analysis and modelling of many different types of experiments. 

Machado, M.L., Mendez, E.P., Sanchez, M.S., Montelongo, F.G. Interpretation of heavy metal data from mussel by use of multivariate classification techniques. Chemoshere 38, 1103-1111 (1999)... 

Ren, S. (20031) Two-step multivariate classification of the mechanisms of toxic action of phenols. QSAR Comb. Sci., 22, 596—603. 

A multivariate classification model was created with the above data. Soft-independent-modeling-class-analogy (SIMCA) uses PCA to model the shape and position of the samples. An acceptance region is then created for each different type of class. SIMCA models also provide interclass distances between samples, these distances are reported on Table II. 

Early attempts at applying chemometric methods to mass spectrometry data sets have been reported see for example [28, 29]. The potential and limitations of using multivariate classification methods for substructure analysis of low resolution mass spectra have also been published [30]. 

The absolute accuracy of our FobIS measurement system is not comparable to commercial lab equipment, and there are important variations in SNR, depending on the impedance values and frequency measured. Nonetheless, we can use it to acquire impedance data in the field with good reproducibility. These data can be used to compute features for classification, i.e. gas discrimination. As usual in multivariate classification, features are derived from the measured raw data, e.g. mean values or slopes of the impedance spectrum or the resistance response curve in TCO [20]. 

Techniques LC-ESI-MS/MS, selected reaction monitoring (SRM), advanced multivariate classification techniques. 

Statistical Techniques Experimental Design Optimization Strategies Multivariate Classification Techniques Multivariate Calibration Techniques Expert Systems Multicriterla Decision Making Signal Processing... 

See also Chemometrics and Statistics Statistical Techniques Multivariate Classification Techniques Multicriteria Decision Making. 

See alsa Chemometrics and Statistics Multivariate Classification Techniques. Forensic Sciences Drug Screening in Sport Illicit Drugs Thin-Layer Chromatography. Gas Chromatography Overview Mass Spectrometry Forensic Applications. Liquid Chromatography Clinical Applications. Microscopy Applications Forensic. Spot Tests. 

See also Chemometrics and Statistics Statistical Techniques Multivariate Classification Techniques Multivariate Calibration Techniques. Food and Nutritional Analysis Overview. Fourier Transform Techniques. Fuels Oil-Based. Infrared Spectroscopy Overview. Pharmaceutical Analysis Drug Purity Determination. Process Analysis Ovenriew. Proteins Foods. Quality Assurance Ouality Control. Textiles Natural Synthetic. 

See also Chemometrics and Statistics Multivariate Classification Techniques. Cosmetics and Toiletries. 

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