公开(公告)号：US20230268082A1

公开(公告)日：2023-08-24

申请号：US18173050

申请日：2023-02-22

Applicant: Hyperspectral Corp.

Inventor： Sarah Rachel Hernandez ,  Matthew Theurer ,  Mario Martinez

IPC: G16H70/60 ,  G01N21/31

CPC classification number: G16H70/60 ,  G01N21/31

Abstract: An example method includes receiving multiple spectrometer scans of a sample obtained from a person. A spectrometer scan includes intensities of wavelengths of light in a range of wavelengths. The light has passed through at least a portion of the sample. For multiple wavelengths of light in the range of wavelengths, a particular profile intensity utilizing particular intensities of wavelengths of light included in the multiple spectrometer scans is calculated to obtain multiple profile intensities. Slopes of the multiple profile intensities at multiple wavelengths are calculated to obtain a set of slopes. A fitting function is applied to the set of slopes to obtain a set of values. A set of decision trees is applied to the set of values to obtain a result. The result indicates either a positive pathogen detection or a negative pathogen detection for the sample. A pathogen detection notification is generated indicating either the positive pathogen detection or the negative pathogen detection for the sample. The pathogen detection notification is provided.

公开(公告)号：US20240142359A1

公开(公告)日：2024-05-02

申请号：US18495726

申请日：2023-10-26

Applicant: Hyperspectral Corp.

Inventor： Sarah Rachel Hernandez ,  Matthew Theurer

IPC: G01N15/02 ,  G01N15/06 ,  G01N35/00

CPC classification number: G01N15/0211 ,  G01N15/0656 ,  G01N35/00732 ,  G01N35/00871 ,  G01N2015/03

Abstract: An example method includes receiving data that includes a set of spectral metrics from interactions of electromagnetic radiation with a sample. A first trained model and a second trained model is applied to at least one of the set of spectral metrics and a set of values based on the set of spectral metrics to obtain a first result and a second result. Based on at least one of the first result and the second result, either a positive particle of interest detection or a negative particle of interest detection for at least one of first particles of interest, a first type of the first particles of interest, and a second type of the first particles of interest for the sample is determined. A particle of interest detection notification that indicates either the positive particle of interest detection or the negative particle of interest detection is generated and provided.

公开(公告)号：US20230266236A1

公开(公告)日：2023-08-24

申请号：US18173035

申请日：2023-02-22

Applicant: Hyperspectral Corp.

Inventor： Euan Mowat ,  Matthew Theurer ,  Sarah Rachel Hernandez ,  Mario Martinez

IPC: G01N21/31 ,  G01N21/27

CPC classification number: G01N21/314 ,  G01N21/274 ,  G01N2021/317 ,  G01N2201/12784 ,  G01N2201/0484 ,  G01N2201/061 ,  G01N2021/0125

Abstract: An example system includes a light intensity measuring apparatus couplable to a food processing apparatus and a computing system. The light intensity measuring apparatus includes a chamber configured to receive a water sample from the food processing apparatus, a light source, a detector configured to detect light that has passed through the water sample and measure multiple times intensities of wavelengths of the light to obtain multiple sets of measured intensities of wavelengths, and a communication module configured to provide the multiple sets of measured intensities of wavelengths. The computing system may receive the multiple sets of measured intensities, process the multiple sets to obtain a set of values, apply a first set of decision trees to the set of values to obtain a first result indicating a positive or negative foodborne pathogen detection, generate a notification indicating either the positive of negative detection, and provide the notification.

公开(公告)号：US20240019378A1

公开(公告)日：2024-01-18

申请号：US18346749

申请日：2023-07-03

Applicant: Hyperspectral Corp.

Inventor： Matthew Theurer ,  Sarah Rachel Hernandez

IPC: G01N21/78 ,  G01N21/31 ,  G01N33/02

CPC classification number: G01N21/78 ,  G01N21/31 ,  G01N33/02 ,  G01N2201/129

Abstract: An example method includes receiving a first set of values based on a set of intensity measurements. The set of intensity measurements may be obtained by a light intensity measuring apparatus that measured intensities of light that passed through a sample of a food processing byproduct. A second set of values based on the first set of values may be generated. A set of trained decision trees may be applied to the second set of values to obtain a result. Based on the result, either a positive foodborne pathogen detection or a negative foodborne pathogen detection for a foodborne pathogen in the sample of the food processing byproduct may be determined. A foodborne pathogen detection notification that indicates either the positive foodborne pathogen detection or the negative foodborne pathogen detection for the foodborne pathogen in the sample of the food processing byproduct may be generated and provided.