Spectroscopic characterization of seafood
A method and apparatus for field spectroscopic characterization of seafood is disclosed. A portable NIR spectrometer is connected to an analyzer configured for performing a multivariate analysis of reflection spectra to determine qualitatively the true identities or quantitatively the freshness of seafood samples.
1. A system comprising:
one or more devices to:
receive information associated with a first type of seafood and a second type of seafood;
determine a first plurality of spectra for the first type of seafood and a second plurality of spectra for the second type of seafood;
perform a first operation on the first plurality of spectra to generate a modified first plurality of spectra;
perform a second operation on the second plurality of spectra to generate a modified second plurality of spectra; and
generate, based on performing the first operation and the second operation, a model,
information associated with the model being used to identify a seafood sample based on a reflection spectrum obtained by a spectrometer, and
the spectrometer being separate from the one or more devices.
2. The system of claim 1 , where
the first type of seafood and the second type of seafood have similar visual features,
the first type of seafood is a first species, and
the second type of seafood is a second species.
3. The system of claim 1 , where
the first plurality of spectra includes a first quantity of spectra for a first characteristic of the first type of seafood and a second quantity of spectra for a second characteristic of the first type of seafood, and
the second plurality of spectra includes a third quantity of spectra for a first characteristic of the second type of seafood and a fourth quantity of spectra for a second characteristic of the second type of seafood.
4. The system of claim 1 , where the one or more devices are further to:
receive information associated with a plurality of additional types of seafood,
each additional type of seafood, of the plurality of additional types of seafood, being different than the first type of seafood and being different than the second type of seafood;
generate a plurality of spectra for each additional type of seafood; and
perform an operation on the plurality of spectra for each additional type of seafood to generate a modified plurality of spectra for each additional type of seafood,
information associated with the modified plurality of spectra for each additional type of seafood being included in the model.
5. The system of claim 1 , where the one or more devices are further to:
perform, using the information associated with the model, multivariate pattern recognition analysis on the seafood sample; and
transmit, based on performing the multivariate pattern recognition analysis, a result of the multivariate pattern recognition analysis to another device for display.
6. The system of claim 1 , where information associated with the seafood sample is obtained using the spectrometer.
7. The system of claim 1 , where the one or more devices are further to:
identify, using the information associated with the model, a type of seafood associated with the seafood sample.
8. A method comprising:
receiving, by a device, information associated with a first type of seafood and a second type of seafood;
determining, by the device, a first plurality of spectra for the first type of seafood and a second plurality of spectra for the second type of seafood;
performing, by the device, a first operation on the first plurality of spectra to generate a modified first plurality of spectra;
performing, by the device, a second operation on the second plurality of spectra to generate a modified second plurality of spectra; and
generating, by the device, and based on performing the first operation and the second operation, a model,
information associated with the model being used to identify a seafood sample based on a reflection spectrum obtained by a spectrometer, and
the spectrometer being separate from the device.
9. The method of claim 8 , where
the first type of seafood and the second type of seafood have similar visual features,
the first type of seafood is a first species, and
the second type of seafood is a second species.
10. The method of claim 8 , where
the first plurality of spectra includes a first quantity of spectra for a first characteristic of the first type of seafood and a second quantity of spectra for a second characteristic of the first type of seafood, and
the second plurality of spectra includes a third quantity of spectra for a first characteristic of the second type of seafood and a fourth quantity of spectra for a second characteristic of the second type of seafood.
11. The method of claim 8 , further comprising:
receiving information associated with a plurality of additional types of seafood,
each additional type of seafood, of the plurality of additional types of seafood, being different than the first type of seafood and being different than the second type of seafood;
generating a plurality of spectra for each additional type of seafood; and
performing an operation on the plurality of spectra for each additional type of seafood to generate a modified plurality of spectra for each additional type of seafood,
information associated with the modified plurality of spectra for each additional type of seafood being included in the model.
12. The method of claim 8 , further comprising:
performing, using the information associated with the model, multivariate pattern recognition analysis on the seafood sample; and
providing, based on performing the multivariate pattern recognition analysis, a result of the multivariate pattern recognition analysis for display.
13. The method of claim 8 , where information associated with the seafood sample is obtained using the spectrometer.
14. The method of claim 8 , further comprising:
identifying, using the information associated with the model, a type of seafood associated with the seafood sample.
15. A non-transitory computer readable medium storing instructions, the instructions comprising:
one or more instructions which, when executed by a processor of a device, cause the processor to:
receive information associated with a first type of seafood and a second type of seafood;
determine a first plurality of spectra for the first type of seafood and a second plurality of spectra for the second type of seafood;
perform a first operation on the first plurality of spectra to generate a modified first plurality of spectra;
perform a second operation on the second plurality of spectra to generate a modified second plurality of spectra; and
generate, based on performing the first operation and the second operation, a model,
information associated with the model being used to identify a seafood sample based on a reflection spectrum obtained by a spectrometer, and
the spectrometer being separate from the device.
16. The non-transitory computer readable medium of claim 15 , where
the first plurality of spectra includes a first quantity of spectra for a first characteristic of the first type of seafood and a second quantity of spectra for a second characteristic of the first type of seafood, and
the second plurality of spectra includes a third quantity of spectra for a first characteristic of the second type of seafood and a fourth quantity of spectra for a second characteristic of the second type of seafood.
17. The non-transitory computer readable medium of claim 15 , where the instructions further include:
one or more instructions to receive information associated with a plurality of additional types of seafood,
each additional type of seafood, of the plurality of additional types of seafood, being different than the first type of seafood and being different than the second type of seafood;
one or more instructions to generate a plurality of spectra for each additional type of seafood; and
one or more instructions to perform an operation on the plurality of spectra for each additional type of seafood to generate a modified plurality of spectra for each additional type of seafood,
information associated with the modified plurality of spectra for each additional type of seafood being included in the model.
18. The non-transitory computer readable medium of claim 15 , where the instructions further include:
one or more instructions to perform, using the information associated with the model, multivariate pattern recognition analysis on the seafood sample; and
one or more instructions to provide, based on performing the multivariate pattern recognition analysis, a result of the multivariate pattern recognition analysis for display.
19. The non-transitory computer readable medium of claim 15 , where information associated with the seafood sample is obtained using the spectrometer.
20. The non-transitory computer readable medium of claim 15 , where the instructions further include:
one or more instructions to identify, using the information associated with the model, a type of seafood associated with the seafood sample.