Diagnostics pattern recognition

Frequently the efforts are hampered by the lack of a sufficiently large number of samples. In our experience, it appears to be helpful if the number of samples exceeds the number of parameters (wavenumber intervals, principal components, latent variables, etc.) by at least a factor of five. This finding is supported by calculating this ratio between the number of teaching samples and the number of parameters both in the field of diagnostic pattern recognition (see the column ratio in Table 6.2) as well as in the quantitative analysis of serum. 

Table 6.2 Diagnostic Pattern Recognition (DPR) in various applications (R-LDA, robust linear discriminant analysis LDA, linear discriminant analysis QDA, quadratic discriminant analysis RDA, regularised discriminant analysis ANN, artificial neural network PCA, principal component analysis SVM, support vector machine Nteach. number of teaching samples Npara, number of parameters used for classification (principal components etc)-, ratio, Nteach/Nparal Nvai, number of independent validation samples SE, sensitivity SP, specificity LOO, leave-one-out validation. AMI,... 

Furthermore, as a fuel evaporates or biodegrades, its pattern can change so radically that identification becomes difficult. Consequently, a gas chromatographic fingerprint is not a conclusive diagnostic tool. The methods used for total petroleum hydrocarbon analysis must stress calibration and quality control, whereas pattern recognition methods stress detail and comparability. 

PCA and HCA (Sample Diagnostic) In addition to the KNN numerical results, it is instructive to use other pattern-recognition tools to examine the data. As discussed in Sections 4.2.1 and 4.2.2, HCA and/or PCA can be helpful for visualizing multivariate data to better understand the clustering (see Section 4.2.1.1 for HCA results using a superset of these data). 

Our own efforts to miniaturize these instruments are driven by two major interests biological agent detection and clinical diagnostics. The development of a biological threat sensor (rather than a chemical sensor) has been addressed in the past by instruments using pattern recognition techniques for spectra... 

Elimination efficiencies for surfactants as predominantly observed pollutants in a conventional and in parallel three bio-membrane assisted wastewater treatment plants were studied. ESI-FIA and LC-MS and MS/MS in negative mode were used to qualify and quantify LAS. Diagnostic scans were appHed for confirmation [343]. The elimination efficiency of LAS in a wet air oxidation reactor by chemical treatment of a wastewater was monitored by ESI-FIA-MS(-) applying a pattern recognition [471]. 

There are other fluorimetric techniques of interest to clinical and pharmaceutical analyses. Thus, the performance of total fluorescence spectrometry in multicomponent and complex analyses has been assessed. The overall fluorescence of human serum and urine can be used as a diagnostic tool in clinical chemistry, particularly in view of its selectivity towards even quite small changes in the location of the peaks and their relative intensities. This is the foundation of pattern recognition methods for detection of pathological deviations from normal status. This technique is also suitable for rapid screening of a variety of drugs since the samples require virtually no treatment. 

Various signal-processing and pattern-recognition techniques are used for analysis of these data for fhe purposes of human identification or verification. Similar techniques can be deployed in behavioral screening, as well as biomedical applications, except that the purpose is not identification of an individual but analysis of the individual s biometrics for security-relafed issues and medical prediagnostics or diagnostic purposes. 

The pattern recognition of peptides and proteins has received increasing attention, due to the potential applications in the fields of proteomic, diagnostic or bioclinical analyses, of paramount importance in biological and medical research [71]. 

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