Machine-learning method and apparatus to isolate chemical signatures
AssigneeOregon State University
InventorGerrad Jones
Life of this patent
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Abstract
A processing workflow centered on machine-learning algorithms that identifies a number of chemical features that can best distinguish the presence or absence of a chemical source. These chemical features are a chemical fingerprint that is unique to each source. The analysis workflow is rapid (e.g., fingerprints can be generated in minutes). The analysis workflow has wide-ranging applications such as detecting markers of pollution sources in rivers and fish tissues, forest pathogen outbreaks, and hard-to-diagnose diseases.
Claims20
A machine-readable storage media having machine-executable instructions that, when executed, cause one or more machines to perform a method for distinguishing a presence or absence of an individual chemical source of at least two chemical sources, the method comprising:
- binning a mass spectral data set obtained with a mass spectrometer for each individual chemical source of the at least two chemical sources into an individual bin to generate a binned source, wherein the binned source corresponds to an individual category of the at least two chemical sources;
- converting the individual bin into a binary variable comprising 1s and 0s for the individual chemical source, wherein 1s represent one or more samples from a chemical source of interest and 0s represent other chemical sources from the at least two chemical sources, or vice versa;
- and
- selecting, based on a specification of a spectral signature derived from the mass spectral data set associated with the individual category, one or more representative sample sites.
An apparatus comprising:
- a high-resolution mass spectrometer to analyze a plurality of samples from at least two chemical sources;
- and
- one or more processors communicatively coupled to the high-resolution mass spectrometer, wherein the one or more processors is to:
- bin a mass spectral data set obtained with a mass spectrometer for each individual chemical source of the at least two chemical sources into an individual bin to generate a binned source, wherein the binned source corresponds to an individual category of the at least two chemical sources;
- convert the individual bin into a binary variable comprising 1s and 0s for the individual chemical source, wherein 1s represent one or more samples from a chemical source of interest and 0s represent other chemical sources from the at least two chemical sources, or vice versa;
- and
- select, based on a specification of a spectral signature derived from the mass spectral data set associated with the individual category, one or more representative sample sites.
A method for distinguishing a presence or absence of an individual chemical source of at least two chemical sources, the method comprising:
- binning a mass spectral data set obtained with a mass spectrometer for each individual chemical source of the at least two chemical sources into an individual bin to generate a binned source, wherein the binned source corresponds to an individual category of the at least two chemical sources;
- converting the individual bin into a binary variable comprising 1s and 0s for the individual chemical source, wherein 1s represent one or more samples from a chemical source of interest and 0s represent other chemical sources from the at least two chemical sources, or vice versa;
- and
- selecting, based on a specification of a spectral signature derived from the mass spectral data set associated with the individual category, one or more representative sample sites.
Dependencies are inferred from the claim text.