Reduced false positive identification for spectral classification
By using support vector machines to generate classification models and introducing mismatch categories, combined with confidence measurement and local modeling technology, the problem of false positive identification in spectral classification is solved and the accuracy of spectroscopy is improved.
CN113989603BActive Publication Date: 2025-09-30VIAVI SOLUTIONS INC(US)
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Patent Information
- Application Number
- CN202111346296.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Priority Date
- 2018-09-13
- Filing Date
- 2019-01-21
- Publication Date
- 2025-09-30
- Estimated Expiration
- 2039-01-21
AI Technical Summary
Technical Problem
Existing spectral classification techniques are prone to false positive identification, especially when the spectrometer is operated incorrectly or the samples do not match, resulting in unknown samples being misclassified.
Method used
Support vector machine (SVM) technology is used to generate a classification model, and mismatch categories are introduced. Confidence measurement and local modeling techniques are used to reduce false positive identification and ensure that unknown samples are not misclassified.
Benefits of technology
The accuracy of spectral classification is improved, the possibility of false positive identification is reduced, and the accuracy of spectroscopy is enhanced.
✦ Generated by Eureka AI based on patent content.
Abstract
The present disclosure relates to reduced false positive identification for spectral classification. A device may receive information identifying a result of a set of spectral measurements of a training set of known samples and a validation set of known samples. The device may generate a classification model based on the information identifying the result of the set of spectral measurements, wherein the classification model includes at least one class associated with a material of interest for spectrometric determination, and wherein the classification model includes a mismatch class associated with at least one material of no interest or a baseline spectral measurement. The device may receive information identifying a particular result of a particular spectral measurement of an unknown sample. The device may determine whether the unknown sample is included in the mismatch class using the classification model. The device may provide an output indicating whether the unknown sample is included in the mismatch class.
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