SYSTEMS AND METHODS FOR PARTICLE OF INTEREST DETECTION

    公开(公告)号:US20240145040A1

    公开(公告)日:2024-05-02

    申请号:US18496650

    申请日:2023-10-27

    CPC classification number: G16B40/10 G01N21/94 G16B15/00

    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, trained models being different from each other; applying the results to a trained multi-model to determine a positive or a negative pathogen detection for the pathogen in the sample, generating a notification and providing the notification.

    SYSTEMS AND METHODS FOR DETECTING FOODBORNE PATHOGENS BY ANALYZING SPECTRAL DATA

    公开(公告)号:US20240019378A1

    公开(公告)日:2024-01-18

    申请号:US18346749

    申请日:2023-07-03

    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.

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