Reduced false positive identification for spectroscopic classification
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.
1. A method comprising:
determining, by a device comprising one or more processors, that an unknown sample is an outlier sample by using an aggregated classification model;
determining, by the device, that one or more spectroscopic measurements are not performed accurately based on determining that the unknown sample is the outlier sample; and
causing, by the device, one or more actions based on determining that the one or more spectroscopic measurements are not performed accurately.
2. The method of claim 1 , wherein causing the one or more actions comprises:
receiving, based on determining that the one or more spectroscopic measurements are not performed accurately, another set of results identifying another set of spectroscopic measurements.
3. The method of claim 1 , further comprising:
identifying, based on causing the one or more actions, unknown spectra differing from the aggregated classification model by a threshold amount.
4. The method of claim 1 , further comprising:
determining that one or more different spectroscopic measurements are performed accurately; and
classifying, based on determining that the one or more different spectroscopic measurements are performed accurately, whether the unknown sample is included in a no-match class for the aggregated classification model.
5. The method of claim 4 , further comprising:
providing an output indicating that the unknown sample is included in the no-match class.
6. The method of claim 1 , further comprising:
determining whether the unknown sample is a nuisance or atypical material when performing an in-situ thresholding technique.
7. The method of claim 1 , further comprising:
cross-validating decision values associated with a first class of the unknown sample and/or a second class of the unknown sample; and
using the decision values to set an upper bound and lower bound for a prediction threshold.
8. A device, comprising:
one or more memories; and
one or more processors, coupled to the one or more memories, configured to:
determine that an unknown sample is an outlier sample by using an aggregated classification model;
determine that one or more spectroscopic measurements are not performed accurately based on determining that the unknown sample is the outlier sample; and
cause one or more actions based on determining the one or more spectroscopic measurements are not performed accurately.
9. The device of claim 8 , wherein the one or more processors, to cause the one or more actions, are configured to:
receive, based on determining that the one or more spectroscopic measurements are not performed accurately, another set of results identifying another set of spectroscopic measurements.
10. The device of claim 8 , wherein the one or more processors are further configured to:
identify, based on causing the one or more actions, unknown spectra differing from the aggregated classification model by a threshold amount.
11. The device of claim 8 , wherein the one or more processors are further configured to:
determine that one or more different spectroscopic measurements are performed accurately; and
classify, based on determining that the one or more different spectroscopic measurements are performed accurately, whether the unknown sample is included in a no-match class for the aggregated classification model.
12. The device of claim 11 , wherein the one or more processors are further configured to:
provide an output indicating that the unknown sample is included in the no-match class.
13. The device of claim 8 , wherein the one or more processors are further configured to:
determine whether the unknown sample is a nuisance or a typical material when performing an in-situ thresholding technique.
14. The device of claim 8 , wherein the one or more processors are further configured to:
cross-validate decision values associated with a first class of the unknown sample and/or a second class of the unknown sample; and
use the decision values to set an upper bound and lower bound for a prediction threshold.
15. A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising:
one or more instructions that, when executed by one or more processors of a device, cause the device to:
determine that an unknown sample is an outlier sample by using an aggregated classification model;
determine that one or more spectroscopic measurements are not performed accurately based on determining that the unknown sample is the outlier sample; and
cause one or more actions based on determining the one or more spectroscopic measurements are not performed accurately.
16. The non-transitory computer-readable medium of claim 15 , wherein the one or more instructions, that cause the device to cause the one or more actions, cause the device to:
receive, based on determining that the one or more spectroscopic measurements are not performed accurately, another set of results identifying another set of spectroscopic measurements.
17. The non-transitory computer-readable medium of claim 15 , wherein the one or more instructions further cause the device to:
identify, based on causing the one or more actions, unknown spectra differing from the aggregated classification model by a threshold amount.
18. The non-transitory computer-readable medium of claim 15 , wherein the one or more instructions further cause the device to:
determine that one or more different spectroscopic measurements are performed accurately; and
classify, based on determining that the one or more different spectroscopic measurements are performed accurately, whether the unknown sample is included in a no-match class for the aggregated classification model.
19. The non-transitory computer-readable medium of claim 18 , wherein the one or more instructions further cause the device to:
provide an output indicating that the unknown sample is included in the no-match class.
20. The non-transitory computer-readable medium of claim 15 , wherein the one or more instructions further cause the device to:
determine whether the unknown sample is a nuisance or atypical material when performing an in-situ thresholding technique.