Discrimination Analysis Used with Optical Computing Devices
First Claim
1. A method, comprising:
- optically interacting a plurality of optical elements with one or more known substances, each optical element being configured to detect a particular characteristic of the one or more known substances;
generating an optical response from each optical element corresponding to each known substance, wherein each known substance corresponds to a known spectrum stored in an optical database; and
training a neural network to provide a discriminant analysis classification model for an unknown substance, the neural network using each optical response as inputs and one or more fluid types as outputs, and the outputs corresponding to the one or more known substances.
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Abstract
Disclosed are systems and methods that use discriminant analysis techniques and processing in order to reduce the time required to determine chemical and/or physical properties of a substance. One method includes optically interacting a plurality of optical elements with one or more known substances, each optical element being configured to detect a particular characteristic of the one or more known substances, generating an optical response from each optical element corresponding to each known substance, wherein each known substance corresponds to a known spectrum stored in an optical database, and training a neural network to provide a discriminant analysis classification model for an unknown substance, the neural network using each optical response as inputs and one or more fluid types as outputs, and the outputs corresponding to the one or more known substances.
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Citations
18 Claims
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1. A method, comprising:
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optically interacting a plurality of optical elements with one or more known substances, each optical element being configured to detect a particular characteristic of the one or more known substances; generating an optical response from each optical element corresponding to each known substance, wherein each known substance corresponds to a known spectrum stored in an optical database; and training a neural network to provide a discriminant analysis classification model for an unknown substance, the neural network using each optical response as inputs and one or more fluid types as outputs, and the outputs corresponding to the one or more known substances. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A method, comprising:
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training a neural network having one or more outputs corresponding to one or more known substances, thereby generating a multivariate classification model for discriminant analysis of an unknown substance; introducing into the multivariate classification model an unknown substance optical response corresponding to the unknown substance; and outputting a predicted substance type corresponding to the unknown substance from the one or more outputs. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17, 18)
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Specification