Expert system based on principal components analysis for the identification of molecular structures from vapor-phase infrared spectra. 2. Identification of carbonyl-containing functionalities

1992 ◽  
Vol 64 (6) ◽  
pp. 656-663 ◽  
Author(s):  
Erik J. Hasenoehrl ◽  
Jonathan H. Perkins ◽  
Peter R. Griffiths
Keyword(s):  
Expert System ◽  
Principal Components Analysis ◽  
Vapor Phase ◽  
Infrared Spectra ◽  
Principal Components ◽  
Molecular Structures ◽  
Components Analysis

Classification of Condensed-Phase Infrared Spectra by Substructures Using Principal Components Analysis

1993 ◽  
Vol 47 (5) ◽  
pp. 643-650 ◽  
Author(s):  
Erik J. Hasenoehrl ◽  
Peter R. Griffiths
Keyword(s):  
Expert System ◽  
Principal Components Analysis ◽  
Infrared Spectra ◽  
Condensed Phase ◽  
Principal Components ◽  
Classification Rule ◽  
Chromatographic Separations ◽  
Ft Ir ◽  
Characteristic Group ◽  
Components Analysis

Expert system rules have been developed through the use of principal components analysis (PCA) for discriminating compounds containing large molecular substructures from their condensed-phase infrared spectra. With the use of this approach, the presence of substructures such as those of barbiturates, cocaines, and amphetamines can be recognized automatically. The classification rule can be generated from a PCA of a small training set of infrared spectra of compounds containing the substructure of interest. One important use of this type of expert system is the analysis of direct-deposition capillary gas and supercritical fluid chromatographic separations in which many peaks are eluted and analyzed by FT-IR spectrometry but only one or two contain the substructure of interest. Classification rules for substructures are easy to generate with little or no knowledge of characteristic group frequencies.

PRINCE: An Expert System for Nonlinear Principal Components Analysis

Compstat ◽  
1988 ◽  
pp. 149-153 ◽  
Author(s):  
I. J. Duijsens ◽  
T. J. Duijkers ◽  
G. M. van den Berg
Keyword(s):  
Expert System ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Components Analysis

Hearing Aid Troubleshooting Based on Patients' Descriptions

2003 ◽  
Vol 14 (07) ◽  
pp. 347-360 ◽  
Author(s):  
Lorienne M. Jenstad ◽  
Dianne J. Van Tasell ◽  
Chiquita Ewert
Keyword(s):  
Expert System ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Hearing Aid ◽  
Fine Tuning ◽  
Hearing Aid Fitting ◽  
Patient Reports ◽  
Components Analysis ◽  
High Degree

Clinical audiologists were surveyed to determine the terms that their patients use to describe their reaction to hearing aid fitting problems. From this survey, a vocabulary of 40 frequently reported terms was developed. A second survey of hearing aid fitting experts was conducted to determine their methods of troubleshooting a hearing aid fitting when the patient reports one of the frequently reported terms. There was a high degree of agreement among the 24 experts. Principal components analysis resulted in ten components that explained 90.8% of the variance in the experts' responses. These results can be applied as an expert system for fine-tuning hearing aid fittings.

Diagnoses Generated by Numerical Taxonomic Methods Applied to Standard Blood Variables

1980 ◽  
Vol 19 (04) ◽  
pp. 205-209
Author(s):  
L. A. Abbott ◽  
J. B. Mitton
Keyword(s):  
Liver Cirrhosis ◽  
Renal Disease ◽  
Principal Components Analysis ◽  
Discriminant Function ◽  
Principal Components ◽  
Discriminant Function Analysis ◽  
Chronic Renal Disease ◽  
Function Analysis ◽  
Infectious Hepatitis ◽  
Components Analysis

Data taken from the blood of 262 patients diagnosed for malabsorption, elective cholecystectomy, acute cholecystitis, infectious hepatitis, liver cirrhosis, or chronic renal disease were analyzed with three numerical taxonomy (NT) methods : cluster analysis, principal components analysis, and discriminant function analysis. Principal components analysis revealed discrete clusters of patients suffering from chronic renal disease, liver cirrhosis, and infectious hepatitis, which could be displayed by NT clustering as well as by plotting, but other disease groups were poorly defined. Sharper resolution of the same disease groups was attained by discriminant function analysis.

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