Systems and Methods for Compact and Low-Cost Vibrational Spectroscopy Platforms
AssigneeThe Board of Trustees of the Leland Stanford Junior University
InventorsJennifer A. Dionne, Ahmed Shuaibi, Amr A. E. Saleh
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Abstract
Systems and methods for compact and low-cost vibrational spectroscopy platforms are described. Many embodiments implement deep learning processes to identify the relevant optical spectral features for the identification of an element from a set of elements. Several embodiments provide that resolution reduction and feature selection render efficient data analysis processes. By reducing the spectral data from the full wide-band high-resolution spectrum to a subset of spectral bands, a number of embodiments provide compact and low-cost hardware incorporation in spectroscopic platforms for element identification functions.
Claims22
A vibrational spectroscopy platform comprising:
- a sample light source;
- an image sensor disposed a set distance from a sample;
- and
- at least one optical filter disposed in line with the image sensor;
- wherein the sample light source is configured to deliver a full vibrational spectrum of the sample to the image sensor;
- and
- wherein a light from the sample light source passes through the at least one optical filter prior to reaching the image sensor, and
- wherein the at least one optical filter selects a set of spectral bands from the full vibrational spectrum of the sample for detection by the image sensor such that the set distance between the image sensor and the sample is shorter than required for detection of the full vibrational spectrum.
A method to identify a pathogen using a Raman spectrometer comprising:
- obtaining a plurality of Raman spectra of pathogens as input;
- applying a feature selection process to the plurality of Raman spectra to select a plurality of features on a computer;
- classifying and ranking the plurality of features by classification accuracy;
- determining a set of features based on the ranking as output;
- and
- applying the set of features to identify the pathogen;
- wherein the classifying and ranking process comprises training a convolutional neural network with the plurality of features.
Dependencies are inferred from the claim text.