Pattern recognition methods steps

Pattern recognition methods are usually applied in discrete steps, which are outlined here. It is assumed that the chemical measurements... 

In contras to unsupervised methods, supervised pattern-recognition methods (Section 4.3) use class membership information in the calculations. The goal of these methods is to construct models using analytical measurements to predict class membership of future samples. Class location and sometimes shape are used in the calibration step to construct the models. In prediction, these moddsare applied to the analytical measurements of unknowu samples to predict dsss membership. 

Flow chart of steps involved in structure-activity studies using chemical structure information, handling and pattern recognition methods... 

The typical steps during an application of pattern recognition methods for a chemical classification problem are ... 

Feature selection is the process by which the data or variables liq>or-tant for class assignment are determined. In this step of a pattern recognition study the various methods differ considerably. In the hyperplane methods, the strategy is to begin with a block of variables for the classes, calculate a classification function, and test it for classification of the training set. In this initial phase, generally many more variables are included than are necessary. Variables are then detected in a stepwise process and a new rule is derived and tested. This process is repeated until a set of variables is obtained that will give an acceptable level of classification. 

In this review, a critical overview of artificial tongue applications over the last decade is outlined. In particular, the focus is centered on the chemometric techniques, which allow the extraction of valuable information from nonspecific data. The basic steps of signal processing and pattern recognition are discussed and the principal chemometric techniques are described in detail, highlighting benefits and drawbacks of each one. Furthermore, some novel methods recently introduced and particularly suitable for artificial tongue data are presented. 

Pattern recognition can be applied for the determination of structural features of unknown (monofunctional) compounds (Huber and Reich ). The information about the chemical structure is contained in a multidimensional gas-liquid retention data/stationary liquid phases set. The linear learning machine method is applied in a two step classification procedure. After the determination of a correction term, the skeleton number, a classification step for the determination of the functional group is executed. It is remarkable that 10 stationary phases are sufficient for the classification. 

Sequential signals are surprisingly widespread in chemistry, and require a large number of methods for analysis. Most data are obtained via computerised instruments such as those for NIR, HPLC or NMR, and raw information such as peak integrals, peak shifts and positions is often dependent on how the information from the computer is first processed. An appreciation of this step is essential prior to applying further multivariate methods such as pattern recognition or classification. Spectra and chromatograms are examples of series that are sequential in time or frequency. However, time series also occur very widely in other areas of chemistry, for example in the area of industrial process control and natural processes. 

The SIMCA method, first advocated by the S. Wold in tire early 1970s, is regarded by many as a form of soft modelling used in chemical pattern recognition. Although there are some differences with linear discriminant analysis as employed in traditional statistics, the distinction is not as radical as many would believe. However, SIMCA has an important role in the history of chemometrics so it is important to understand the main steps of the method. 

The second step of the algorithm is the comparison of descriptor values. Several different methods are applicable to the problem, most of which are adopted from pattern recognition or statistical sciences. 

When there is no spectrum in the library that can be used to elucidate a chemical structure, interpretative methods are needed. The methods of pattern recognition and of artificial intelligence must then be used. As a result, different chemical structures will be obtained as candidates for the unknown molecule. To verify an assumed structure, simulation of spectra becomes important. In a final step, the simulated spectrum could be compared with that measured. 

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