Abstract: A system comprising at least one light source configured to generate a light at specific wavelengths and project the light over an optical path, a sample device, the sample device configured to receive a sample obtained from a person, the sample device being transparent and being at least partially within the optical path, a diversifier including occlusions for scattering coherent light received from the light source along the optical path, a first detector configured to receive the light over the optical path and from at least a portion of the diversifier, the detector configured to detect spectral intensities of the light, and a second detector configured to receive at least a portion of the light form the optical path before the light passes through the diffuser, the second detector configured to detect spectral intensities of the light.

Abstract: An example method comprising: receiving a first set of intensity values based on a first set of intensity measurements for a set of wavelengths, the set of intensity measurements obtained by an apparatus configured to generate light and another light, detect the light that has passed through a portion of a sample, and measure intensity of the light by optical sensor, an angle separating the optical sensor and apparatus; applying a trained model to obtain a result; based on the result, determining a subset of wavelengths; receiving another set of intensity values, another set of intensity values being based on another set of intensity measurements for the set of wavelengths, the other set of intensity measurements obtained by the other light, detect the other light that has passed through another portion of the sample, and measure intensity by the optical sensor, another angle separating the optical sensor and the apparatus, the angles being different, applying another trained model to obtain another result, t

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.

Abstract: A method comprising at least one light source configured to generate a light of at least one wavelength and project the light over an optical path, a sample device, the device containing a sample obtained from exhalation of a person, a vortex mask configured to receive the light after the light passes through at least a portion of the sample device, the vortex mask including a series of concentric circles etched in a substrate, the vortex mask configured to provide destructive interference of coherent light received from the at least one light source, a detector configured to detect and measure wavelength intensities from the light in the optical path, the wavelength intensities being impacted by the light passing through the sample, the detector receiving the light that remained after passing through the vortex mask, and a processor configured to provide measurement results based on the wavelength intensities.

Abstract: A method comprising at least one light source configured to generate a light of at least one wavelength and project the light over an optical path, a sample device, the device containing a sample obtained from exhalation of a person, a vortex mask configured to receive the light after the light passes through at least a portion of the sample device, the vortex mask including a series of concentric circles etched in a substrate, the vortex mask configured to provide destructive interference of coherent light received from the at least one light source, a detector configured to detect and measure wavelength intensities from the light in the optical path, the wavelength intensities being impacted by the light passing through the sample, the detector receiving the light that remained after passing through the vortex mask, and a processor configured to provide measurement results based on the wavelength intensities.

Abstract: An example method includes receiving a first set of data that includes spectral metrics provided by a spectral acquisition apparatus that obtains the spectral metrics based on interactions of electromagnetic radiation with a sample. The first set of data is processed to obtain a second set of data that includes the spectral metrics. One or more trained models are applied to the spectral metrics or a set of values based on the spectral metrics to obtain a result. Based on the result, either a positive particle of interest detection or a negative particle of interest detection for the particle 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 for the particle of interest for the sample may be generated and provided.

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.

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.

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.