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Title: Neural network system and methods for analysis of organic materials and structures using spectral data

Abstract

Apparatus and processes for recognizing and identifying materials. Characteristic spectra are obtained for the materials via spectroscopy techniques including nuclear magnetic resonance spectroscopy, infrared absorption analysis, x-ray analysis, mass spectroscopy and gas chromatography. Desired portions of the spectra may be selected and then placed in proper form and format for presentation to a number of input layer neurons in an offline neural network. The network is first trained according to a predetermined training process; it may then be employed to identify particular materials. Such apparatus and processes are particularly useful for recognizing and identifying organic compounds such as complex carbohydrates, whose spectra conventionally require a high level of training and many hours of hard work to identify, and are frequently indistinguishable from one another by human interpretation.

Inventors:
 [1];  [2];  [3]
+ Show Inventor Affiliations
  1. Athens, GA
  2. Suwanee, GA
  3. Fredricksberg, DK
Issue Date:
Research Org.:
University of Georgia Research Foundation, Inc. (Athens, GA)
Sponsoring Org.:
USDOE
OSTI Identifier:
868814
Patent Number(s):
5218529
Assignee:
University of Georgia Research Foundation, Inc. (Athens, GA)
Patent Classifications (CPCs):
G - PHYSICS G06 - COMPUTING G06N - COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
Y - NEW / CROSS SECTIONAL TECHNOLOGIES Y10 - TECHNICAL SUBJECTS COVERED BY FORMER USPC Y10S - TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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DOE Contract Number:  
FG09-85ER13426; FG09-87ER13810
Resource Type:
Patent
Country of Publication:
United States
Language:
English
Subject:
neural; network; methods; analysis; organic; materials; structures; spectral; data; apparatus; processes; recognizing; identifying; characteristic; spectra; obtained; via; spectroscopy; techniques; including; nuclear; magnetic; resonance; infrared; absorption; x-ray; mass; gas; chromatography; desired; portions; selected; placed; proper; form; format; presentation; input; layer; neurons; offline; trained; according; predetermined; training; process; employed; identify; particular; particularly; useful; compounds; complex; carbohydrates; conventionally; require; level; hours; hard; frequently; indistinguishable; human; interpretation; resonance spectroscopy; gas chromatography; organic compound; organic materials; magnetic resonance; neural network; organic compounds; particularly useful; nuclear magnetic; organic material; gas chromatograph; mass spectroscopy; particular materials; particular material; training process; neural net; desired portions; desired portion; materials via; /702/700/706/

Citation Formats

Meyer, Bernd J, Sellers, Jeffrey P, and Thomsen, Jan U. Neural network system and methods for analysis of organic materials and structures using spectral data. United States: N. p., 1993. Web.
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Meyer, Bernd J, Sellers, Jeffrey P, & Thomsen, Jan U. Neural network system and methods for analysis of organic materials and structures using spectral data. United States.
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Meyer, Bernd J, Sellers, Jeffrey P, and Thomsen, Jan U. Fri . "Neural network system and methods for analysis of organic materials and structures using spectral data". United States. https://www.osti.gov/servlets/purl/868814.
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@article{osti_868814,
title = {Neural network system and methods for analysis of organic materials and structures using spectral data},
author = {Meyer, Bernd J and Sellers, Jeffrey P and Thomsen, Jan U},
abstractNote = {Apparatus and processes for recognizing and identifying materials. Characteristic spectra are obtained for the materials via spectroscopy techniques including nuclear magnetic resonance spectroscopy, infrared absorption analysis, x-ray analysis, mass spectroscopy and gas chromatography. Desired portions of the spectra may be selected and then placed in proper form and format for presentation to a number of input layer neurons in an offline neural network. The network is first trained according to a predetermined training process; it may then be employed to identify particular materials. Such apparatus and processes are particularly useful for recognizing and identifying organic compounds such as complex carbohydrates, whose spectra conventionally require a high level of training and many hours of hard work to identify, and are frequently indistinguishable from one another by human interpretation.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri Jan 01 00:00:00 EST 1993},
month = {Fri Jan 01 00:00:00 EST 1993}
}
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Works referenced in this record:

Neural network applications in chemistry begin to appear
journal, April 1989

Modeling chemical process systems via neural computation
journal, April 1990

A network system for image segmentation
conference, January 1989

A neural network model for selective attention in visual pattern recognition
journal, October 1986

Computational neuroscience
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Computing with neural circuits: a model
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Predicting the secondary structure of globular proteins using neural network models
journal, August 1988

Detection of explosives in checked airline baggage using an artificial neural system
conference, January 1989

Neural Network Models for Promoter Recognition
journal, June 1989

Protein secondary structure prediction with a neural network.
journal, January 1989

Pattern recognition of the 1H NMR spectra of sugar alditols using a neural network
journal, August 1989

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