Pattern-recognition importance sampling

Pattern-Recognition Importance Sampling Minimization (PRISM)... 

MIR techniques have simplified obtaining infrared spectra of many materials important in packaging. These include rubber, plastics, laminations, and components of these materials that find use in pumps, sample packages, and devices. The combination of MIR and computerized pattern recognition techniques can be used for differentiating and classification of flexible packaging polymers such as polyvinyl chloride (PVC), polyvinylidene chloride (PVdC), acrylonitrile (Barex), and CTFE (Aclar) [22]. 

Classification, or the division of data into groups, methods can be broadly of two types supervised and unsupervised. The primary difference is that prior information about classes into which the data fall is known and representative samples from these classes are available for supervised methods. The supervised and unsupervised approaches loosely lend themselves into problems that have prior hypotheses and those in which discovery of the classes of data may be needed, respectively. The division is purely for organization purposes in many applications, a combination of both methods can be very powerful. In general, biomedical data analysis will require multiple spectral features and will have stochastic variations. Hence, the field of statistical pattern recognition [88] is of primary importance and we use the term recognition with our learning and classification method descriptions below. 

The choice of the training set is important in any pattern-recognition study. Each class must be well represented in the training set. Experimental variables must be controlled or otherwise accounted for by the selection of suitable samples that take into account all sources of variability in the data, for example, lot-to-lot variability. Experimental artifacts such as instrumental drift or sloping baseline must be minimized. Features containing information about differences in the source profile of each class must be present in the data. Otherwise, the classifier is likely to discover rules that do not work well on test samples, i.e., samples that are not part of the original data. 

M. H. Lambert and H. A. Scheraga, J. Comput. Chem., 10,817 (1989). Pattern Recognition in the Prediction of Protein Structure. III. An Importance-Sampling Minimization Procedure. 

The matching of outcrop samples with debitage and other artifactual material is an ideal problem for computer-assisted pattern recognition techniques (8). The use of ARTHUR for the analysis of the soapstone from Labrador will be discussed in a future publication, but the important parameters for comparing soapstone are given in Table II. For this table, the samples from a given quarry were taken as one or two groups. The... 

The variations in human metabolic profiles can seldom permit visual observations of meaningful metabolic deviations from the normal. However, large computer systems do have the general capability to extract the distinct features from large data sets, and reduce the bulk of data from capillary GC of numerous patients to a more easily understandable form. Precisely measured retention characteristics and the peak areas form the basis for such comparisons. Pattern recognition methods have been utilized to classify diabetic samples [169,170] and those of virus-infected patients [171] with the aid of training sets from clinically defined cases. In addition, the feature extraction approach [169,170] permits identification of important metabolite peaks in complex chromatograms. 

Among the different chemometric methods, exploratory data analysis and pattern recognition are frequently used in the area of food analysis. Exploratory data analysis is focused on the possible relationships between samples and variables, while pattern recognition studies the behavior between samples and variables [95]. Principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) are the methods most commonly used for exploratory analysis and pattern recognition, respectively. The importance of these statistical tools has been demonstrated by the wide number of works in the field of food science where they have been applied. The majority of the applications are related to the characterization and authentication of olive oil, animal fats, marine and vegetable oils [95], wine [97], fruit juice [98], honey [99], cheese [100,101], and so on, although other important use of statistical tools is the detection of adulterants or frauds [96,102]. 

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