Reduced false positive identification for spectroscopic classification

    公开(公告)号:US10810408B2

    公开(公告)日:2020-10-20

    申请号:US16130732

    申请日:2018-09-13

    Abstract: A device may receive information identifying results of a set of spectroscopic 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 results of the set of spectroscopic measurements, wherein the classification model includes at least one class relating to a material of interest for a spectroscopic determination, and wherein the classification model includes a no-match class relating to at least one of at least one material that is not of interest or a baseline spectroscopic measurement. The device may receive information identifying a particular result of a particular spectroscopic measurement of an unknown sample. The device may determine whether the unknown sample is included in the no-match class using the classification model. The device may provide output indicating whether the unknown sample is included in the no-match class.

    Transfer of a calibration model using a sparse transfer set

    公开(公告)号:US10429240B2

    公开(公告)日:2019-10-01

    申请号:US15614110

    申请日:2017-06-05

    Abstract: A device may obtain a master calibration set, associated with a master calibration model of a master instrument, that includes spectra, associated with a set of samples, generated by the master instrument. The device may identify a selected set of master calibrants based on the master calibration set. The device may obtain a selected set of target calibrants that includes spectra, associated with the subset of the set of samples, generated by the target instrument. The device may create a transfer set based on the selected set of master calibrants and the selected set of target calibrants. The device may create a target calibration set, corresponding to the master calibration set, based on the transfer set. The device may generate, using an optimization technique associated with the transfer set and a support vector regression modeling technique, a transferred calibration model, for the target instrument, based on the target calibration set.

    Transfer of a calibration model using a sparse transfer set

    公开(公告)号:US11378452B2

    公开(公告)日:2022-07-05

    申请号:US16582538

    申请日:2019-09-25

    Abstract: A device may obtain a master calibration set, associated with a master calibration model of a master instrument, that includes spectra, associated with a set of samples, generated by the master instrument. The device may identify a selected set of master calibrants based on the master calibration set. The device may obtain a selected set of target calibrants that includes spectra, associated with the subset of the set of samples, generated by the target instrument. The device may create a transfer set based on the selected set of master calibrants and the selected set of target calibrants. The device may create a target calibration set, corresponding to the master calibration set, based on the transfer set. The device may generate, using an optimization technique associated with the transfer set and a support vector regression modeling technique, a transferred calibration model, for the target instrument, based on the target calibration set.

    IDENTIFICATION USING SPECTROSCOPY
    9.
    发明申请

    公开(公告)号:US20190257746A1

    公开(公告)日:2019-08-22

    申请号:US16405050

    申请日:2019-05-07

    Abstract: A device may receive information identifying results of a spectroscopic measurement of an unknown sample. The device may perform a first classification of the unknown sample based on the results of the spectroscopic measurement and a global classification model. The device may generate a local classification model based on the first classification. The device may perform a second classification of the unknown sample based on the results of the spectroscopic measurement and the local classification model. The device may provide information identifying a class associated with the unknown sample based on performing the second classification.

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