Methods, systems, and media for detecting the presence of an analyte

1. A method for detecting the presence of an analyte, the method comprising:

capturing an image of liquid crystals;

determining a first set of one or more features based on the brightness of pixels in the image;

determining a second set of features based on the image of the liquid crystals,

wherein the second set of features comprises a plurality of outputs of a hidden layer of a trained object detection deep learning convolution neural network that was provided with values based on the pixels in the image as an input;

concurrently providing at least the first set of one or more features and the second set of features to a trained support vector machine,

wherein the support vector machine was trained using features based on images captured of other liquid crystals when exposed to a first analyte and the other liquid crystals when exposed to a second analyte; and

receiving an indication from the support vector machine indicating whether the liquid crystals have been exposed to the first analyte.

2. The method of claim 1 , wherein the second set of features are based on a color image of the liquid crystals, and wherein the first set of one or more features are based on a grayscale image of the liquid crystals.

3. The method of claim 1 , further comprising:

generating a normalized RGB image from the first image of the liquid crystals;

converting the normalized RGB image to a grayscale image;

calculating a plurality of oriented gradients using the grayscale image; and

calculating a histogram of the plurality of oriented gradients, wherein the one or more features comprises values from the histogram of the plurality of oriented gradients.

4. The method of claim 3 , wherein the normalized RGB image is generated from a portion of the image of the liquid crystals, wherein the normalized RGB image has a lower resolution than the image of the liquid crystals.

5. The method of claim 1 , wherein the first analyte is a gas phase analyte and the second analyte is a non-targeted gas phase molecule.

6. The method of claim 5 , wherein the first analyte is DMMP and the second analyte is water vapor.

7. The method of claim 6 , wherein the analyte to be detected is sarin.

8. The method of claim 1 , wherein the first analyte is a liquid phase analyte and the second analyte is a non-targeted liquid phase analyte.

9. The method of claim 8 , wherein the first analyte is a biological analyte.

10. The method of claim 1 , wherein the liquid crystals are disposed within a micro-well.

11. The method of claim 1 , wherein the liquid crystals are in contact with self-assembling monomers.

12. The method of claim 1 , wherein the liquid crystals are in contact with a polymerized target of a bioagent.

13. The method of claim 1 , wherein the liquid crystals form at least one droplet suspended in an aqueous phase.

14. The method of claim 1 , wherein the liquid crystals are disposed within a holding compartment of a substrate over which an aqueous solution is being passed.

15. The method of claim 1 , wherein the liquid crystals are doped with a chiral molecule.

16. A system for detecting the presence of an analyte, the system comprising:

an image sensor; and

a processor that is programmed to:

cause the image sensor to capture an image of liquid crystals;

convert the image of the liquid crystals to grayscale;

determine a first set of one or more features based on the brightness of pixels in the grayscale image;

determine a second set of features based on the image of the liquid crystals,

wherein the second set of features comprises a plurality of outputs of a hidden layer of a trained object detection deep learning convolution neural network that was provided with values based on the pixels in the image as an input;

concurrently provide at least the first set of one or more features and the second set of features to a trained support vector machine,

wherein the support vector machine was trained using features based on images captured of other liquid crystals when exposed to a first analyte and the other liquid crystals when exposed to a second analyte; and

receive an indication from the support vector machine indicating whether the liquid crystals have been exposed to the first analyte.

17. A non-transitory computer readable medium containing computer executable instructions that, when executed by a processor, cause the processor to perform a method for detecting the presence of an analyte, the method comprising:

capturing an image of liquid crystals;

converting the image of the liquid crystals to grayscale;

determining a first set of one or more features based on the brightness of pixels in the grayscale image;

determining a second set of features based on the image of the liquid crystals,

wherein the second set of features comprises a plurality of outputs of a hidden layer of a trained object detection deep learning convolution neural network that was provided with values based on the pixels in the image as an input;

concurrently providing the one or more features to a trained support vector machine,

wherein the support vector machine was trained using images captured of other liquid crystals when exposed to a first analyte and the other liquid crystals when exposed to a second analyte; and

receiving an indication from the support vector machine indicating whether the liquid crystals have been exposed to the first analyte.