Systems and methods for compound concentration sensing in fluids

A computer-implemented method for sensing a concentration of a compound, the method comprising:

• capturing a hyperspectral image of a fluid within a flow cell by a hyperspectral imaging device;
• generating a spectral signal based on the hyperspectral image captured;
• providing the spectral signal as an input to a machine learning network;
• predicting by the machine learning network the concentration of the compound in the fluid;
• computing an average of spectral intensity for each column of pixel data in the hyperspectral image of the fluid;
• generating baseline hyperspectral images of water and baseline hyperspectral images of glucose;
• computing an average of spectral intensity for each column of pixel data in the baseline hyperspectral image of water;
• computing an average of spectral intensity for each column of pixel data in the baseline hyperspectral image of glucose;
• and
• determine a reference correction, by subtracting, for each column, the average spectral intensity from the baseline water hyperspectral image from the corresponding column average spectral intensity of the baseline glucose hyperspectral image.

A non-transitory computer-readable medium storing instructions which, when executed by a processor, cause the processor to perform a computer-implemented method for sensing a concentration of a compound, the method comprising:

• generating a hyperspectral image of a fluid within a flow cell by a hyperspectral imaging device;
• generating a spectral signal based on the hyperspectral image captured;
• providing the digital spectral signal as an input to a machine learning network;
• predicting by the machine learning network a concentration of the compound in the fluid;
• computing an average of spectral intensity for each column of pixel data in the hyperspectral image of the fluid;
• generating baseline hyperspectral images of water and baseline hyperspectral images of glucose;
• computing an average of spectral intensity for each column of pixel data in the baseline hyperspectral image of water;
• computing an average of spectral intensity for each column of pixel data in the baseline hyperspectral image of glucose;
• and
• determine a reference correction, by subtracting, for each column, the average spectral intensity from the baseline water hyperspectral image from the corresponding column average spectral intensity of the baseline glucose hyperspectral image.