Methods and systems for predicting absorbance of a water sample using a machine learning model

A method of training a machine-learning model to predict absorbance of a water sample at a selected wavelength, said method comprising:

• a for each of a plurality of water samples,
• 1) capturing, from an imaging spectrometer comprising an image sensor and an optical dispersive element, a spectrally dispersed image of light transmitted through said water sample;
• 2) obtaining, from a reference spectrophotometer, a reference absorbance measurement of said water sample at said selected wavelength;
• 3) storing a paired training record comprising data corresponding to said spectrally dispersed image and said reference absorbance measurement;
• and
• b) training, using said paired training records, a machine-learning regression model configured to output a predicted absorbance value at said selected wavelength.

A system for analyzing a water sample, said system comprising:

• a) an illumination source arranged to direct light through said water sample;
• b) an imaging spectrometer comprising an image sensor and an optical dispersive element arranged such that said light transmitted through said water sample forms a spectrally dispersed image on said image sensor;
• and
• c) at least one processor operatively engaged with a memory storing instructions that, when executed by said at least one processor, cause said at least one processor to:
• 1) receive image data corresponding to said spectrally dispersed image,
• 2) execute a trained machine-learning regression model using at least a portion of said image data as input, and
• 3) output a predicted absorbance value for said water sample at a selected wavelength.