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.

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.

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.

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.

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.

Abstract: A device may receive, from a plurality of sensors, sensor data relating to a user. The device may include a plurality of types of sensors including a spectrometer and one or more of an accelerometer, a heart rate sensor, a blood pressure sensor, a blood sugar sensor, a perspiration sensor, a skin conductivity sensor, or an imaging sensor. The device may process the sensor data, from the plurality of types of sensors, relating to the user to determine a health condition of the user. The device may provide, via a user interface, information identifying the health condition of the user based on processing the sensor data, from the plurality of types of sensors, relating to the user.

Abstract: A device may receive, from a plurality of sensors, sensor data relating to a user. The device may include a plurality of types of sensors including a spectrometer and one or more of an accelerometer, a heart rate sensor, a blood pressure sensor, a blood sugar sensor, a perspiration sensor, a skin conductivity sensor, or an imaging sensor. The device may process the sensor data, from the plurality of types of sensors, relating to the user to determine a health condition of the user. The device may provide, via a user interface, information identifying the health condition of the user based on processing the sensor data, from the plurality of types of sensors, relating to the user.

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.

Abstract: A medication assurance system for verification of both the medication and the patient is disclosed. A portable spectrometer is used to obtain a light spectrum of the medication. A subject identification or biometric device is used to identify the patient. A controller coupled to the portable spectrometer and the subject identification device identifies the medication by performing a chemometric analysis of the light spectrum. Based on the medication identified and the patient identified, the controller can determine if the medication is to be taken by the patient.

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.

Abstract: A medication assurance system for verification of both the medication and the patient is disclosed. A portable spectrometer is used to obtain a light spectrum of the medication. A subject identification or biometric device is used to identify the patient. A controller coupled to the portable spectrometer and the subject identification device identifies the medication by performing a chemometric analysis of the light spectrum. Based on the medication identified and the patient identified, the controller can determine if the medication is to be taken by the patient.

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.

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.

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.

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.

Abstract: A device may receive, from a plurality of sensors, sensor data relating to a user. The device may include a plurality of types of sensors including a spectrometer and one or more of an accelerometer, a heart rate sensor, a blood pressure sensor, a blood sugar sensor, a perspiration sensor, a skin conductivity sensor, or an imaging sensor. The device may process the sensor data, from the plurality of types of sensors, relating to the user to determine a health condition of the user. The device may provide, via a user interface, information identifying the health condition of the user based on processing the sensor data, from the plurality of types of sensors, relating to the user.

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.

Abstract: A medication assurance system for verification of both the medication and the patient is disclosed. A portable spectrometer is used to obtain a light spectrum of the medication. A subject identification or biometric device is used to identify the patient. A controller coupled to the portable spectrometer and the subject identification device identifies the medication by performing a chemometric analysis of the light spectrum. Based on the medication identified and the patient identified, the controller can determine if the medication is to be taken by the patient.

Abstract: A device may receive, from a plurality of sensors, sensor data relating to a user. The device may include a plurality of types of sensors including a spectrometer and one or more of an accelerometer, a heart rate sensor, a blood pressure sensor, a blood sugar sensor, a perspiration sensor, a skin conductivity sensor, or an imaging sensor. The device may process the sensor data, from the plurality of types of sensors, relating to the user to determine a health condition of the user. The device may provide, via a user interface, information identifying the health condition of the user based on processing the sensor data, from the plurality of types of sensors, relating to the user.

Abstract: A portable spectrometer device includes an illumination source for directing at a sample, and a tapered light pipe (TLP) for capturing light interacting with the sample at a first focal ratio and for delivering the light at a second focal ratio lower than the first focal ratio. A linearly variable filter (LVF) separates the captured light into a spectrum of constituent wavelength signals; and a detector array, including a plurality of pixels, each of the plurality of pixels disposed to receive at least a portion of a plurality of the constituent wavelength signals provides a power reading for each constituent wavelength. Preferably, the TLP is lensed at one end, and recessed in a protective boot with stepped inner walls. The gap between the TLP and LVF is minimized to further enhance resolution and robustness.