System and method for detecting target materials using a vis-nir detector

Abstract

The present disclosure provides systems and methods for determining the presence of a target material in a sample. In general terms, the system and method disclosed herein provide collecting interacted photons from a sample having a target material. The interacted photons are passed through a tunable filter to a VIS-NIR detector where the VIS-NIR detector generates a VIS-NIR hyperspectral image representative of the filtered interacted photons. The hyperspectral image of the filtered interacted photons is analyzed by comparing the hyperspectral image of the filtered interacted phtons to known hyperspectral images to identify the presence of a target material in a sample. The systems and methods disclosed herein provide easy identification of the presence of a target material in a sample.

What is claimed is: 
     
         1 . A system for identifying a target material in a sample, the system comprising:
 a first collection optic configured to collect a plurality of interacted photons that have interacted with the sample;   a tunable filter configured to filter a first plurality of interacted photons collected from the first collection optic into a plurality of wavelengths to generate filtered interacted photons;   a VIS-NIR detector configured to detect the filtered interacted photons, wherein the VIS-NIR detector generates a VIS-NIR hyperspectral image representation of the filtered interacted photons; and   a processor configured to analyze the VIS-NIR hyperspectral image of the filtered interacted photons by comparing the VIS-NIR hyperspectral image of the filtered interacted photons to a database of known VIS-NIR hyperspectral images in order to identify the presence of the target material.   
     
     
         2 . The system of  claim 1 , further comprising:
 a second collection optic configured to collect a second plurality of interacted photons; and   a RGB detector configured to detect the second plurality of interacted photons collected from the second collection optic, wherein the RGB detector is configured to generate a RGB image representation of the second plurality of interacted photons.   
     
     
         3 . The system of  claim 2 , wherein the VIS-NIR hyperspectral image and the RGB image are generated substantially simultaneously. 
     
     
         4 . The system of  claim 1 , further comprising an illumination source, wherein the illumination source is configured to provide photons that interact with the sample to generate the plurality of interacted photons. 
     
     
         5 . The system of  claim 1 , wherein the tunable filter comprises a liquid crystal tunable filter, a multi-conjugate tunable filter, an acousto-optical tunable filter, a Lyot liquid crystal tunable filter, an Evans Split-Element liquid crystal tunable filter, a Solc liquid crystal tunable filter, a Ferroelectric liquid crystal tunable filter, a Fabry Perot liquid crystal tunable filter, or any combination thereof. 
     
     
         6 . The system of  claim 1 , wherein the VIS-NIR detector comprises an InGaAs detector, a CMOS detector, an InSb detector, a MCT detector, an ICCD detector, a CCD detector, or any combination thereof. 
     
     
         7 . The system of  claim 1 , wherein the VIS-NIR detector comprises a focal plane array. 
     
     
         8 . The system of  claim 1 , further comprising a display configured to display analysis information obtained by the system to a user. 
     
     
         9 . The system of  claim 1 , further comprising a user interface configured to receive one or more inputs from a user to interact with the system. 
     
     
         10 . The system of  claim 1 , wherein the processor is further configured to analyze the VIS-NIR hyperspectral image generated from the filtered interacted photons by applying a chemometric technique. 
     
     
         11 . The system of  claim 10 , wherein the chemometric technique comprises principle components analysis, partial least squares discriminate analysis, cosine correlation analysis, Euclidian distance analysis, k-means clustering, multivariate curve resolution, band t. entropy method, mahalanobis distance, adaptive subspace detector, spectral mixture resolution, Bayesian fusion or any combination thereof. 
     
     
         12 . The system of  claim 1 , wherein the system is housed in a portable or handheld unit. 
     
     
         13 . A method for identifying a target material in a sample, the method comprising:
 collecting a plurality of interacted photons from the sample, wherein the plurality of interacted photons have interacted with the sample;   directing a first plurality of interacted photons through a filter to generate a first plurality of filtered photons, wherein the filter separates the first plurality of interacted photons into a plurality of wavelengths;   detecting the first plurality of filtered photons with a VIS-NIR hyperspectral image detector, generating a VIS-NIR hyperspectral image of the first plurality of filtered photons; and   analyzing the VIS-NIR hyperspectral image of the filtered interacted photons by comparing the VIS-NIR hyperspectral image of the filtered interacted photons to a database of known hyperspectral images to identify the presence of the target material.   
     
     
         14 . The method of  claim 13 , further comprising:
 collecting a second plurality of interacted photons;   detecting the second plurality of interacted photons with a RGB detector, and   generating a RGB image representation of the second plurality of interacted photons.   
     
     
         15 . The method of  claim 14 , wherein the VIS-NIR hyperspectral image of the filtered interacted photons and the RGB image are generated simultaneously. 
     
     
         16 . The method of  claim 14 , further comprising illuminating the sample with an illumination source, wherein the illumination source provides photons that interact with the sample to generate the second plurality of interacted photons. 
     
     
         17 . The method of  claim 13 , further comprising illuminating the sample with an illumination source wherein, the illumination source provides photons that interact with the sample to generate the first plurality of interacted photons. 
     
     
         18 . The method of  claim 13 , wherein analyzing the VIS-NIR hyperspectral image further comprises applying a chemometric technique. 
     
     
         19 . A system for identifying an target material in a sample, the system comprising:
 an illumination source configured to provide photons that interact with the sample to generate a plurality of interacted photons;   a first collection optic configured to collect a first plurality of interacted photons where the first plurality of interacted photons includes photons that have interacted with the sample;   a second collection optic configured to collect a second plurality of interacted photons where the second plurality of interacted photons includes photons that have interacted with the sample;   a tunable filter configured to filter the first plurality of interacted photons collected from the first collection optic into a plurality of wavelengths to generate filtered interacted photons;   a VIS-NIR detector configured to detect the filtered interacted photons, wherein the VIS-NIR detector generates a VIS-NIR hyperspectral image of the filtered interacted photons;   a RGB detector configured to detect the second plurality of interacted photons, wherein the RGB detector generates a RGB image representation of the second plurality of interacted photons; and   a processor configured to analyze the VIS-NIR hyperspectral of the filtered interacted photons and compare the VIS-NIR hyperspectral image of the filtered interacted phtons to a database of known VIS-NIR hyperspectral images in order to identify the target material.   
     
     
         20 . The system of  claim 19  wherein the VIS-NIR hyperspectral image and the RGB image are generated simultaneously. 
     
     
         21 . The system of  claim 19 , wherein the tunable filter comprises a liquid crystal tunable filter, a multi-conjugate tunable filter, an acousto-optical tunable filter, a Lyot liquid crystal tunable filter, an Evans Split-Element liquid crystal tunable filter, a Solc liquid crystal tunable filter, a Ferroelectric liquid crystal tunable filter, a Fabry Perot liquid crystal tunable filter, or any combination thereof. 
     
     
         22 . The system of  claim 19 , wherein the VIS-NIR detector comprises a InGaAs detector, a CMOS detector, an InSb detector, a MCT detector, an ICCD detector, a CCD detector, or any combination thereof. 
     
     
         23 . The system of  claim 19 , wherein the VIS-NIR detector comprises a focal plane array. 
     
     
         24 . The system of  claim 19 , further comprising a display configured to display VIS-NIR hyperspectral analysis information, RGB image information, or any combination thereof obtained by the system to a user. 
     
     
         25 . The system of  claim 19 , further comprising a user interface configured to receive one or more inputs from a user to interact with the system. 
     
     
         26 . The system of  claim 19 , wherein the processor is further configured to analyze the VIS-NIR hyperspectral image of the filtered interacted photons by applying a chemometric technique. 
     
     
         27 . The system of  claim 26 , wherein the chemometric technique comprises: principle components analysis, partial least squares discriminate analysis, cosine correlation analysis, Euclidian distance analysis, k-means clustering, multivariate curve resolution, band t. entropy method, mahalanobis distance, adaptive subspace detector, spectral mixture resolution, Bayesian fusion or any combination thereof. 
     
     
         28 . The system of  claim 19 , wherein the system is housed in a portable or handheld unit.