System and computer-implemented method for determining predicted component concentrations of a target mixture

A system for determining predicted component concentrations of a target mixture, the system comprising:

• a mid-infrared waveguide sensor configured to measure absorption spectra;
• and
• a computer comprising a processor and a data storage storing computer program instructions operable to cause the processor to:
• receive, from the mid-infrared waveguide sensor, training absorption spectra for a plurality of training mixtures, the plurality of training mixtures each having one or more components associated with the target mixture and comprises different predetermined component concentrations;
• train a machine learning model using the training absorption spectra to obtain a trained machine learning model, the trained machine learning model being adapted to decompose an absorption spectrum of a mixture into component absorption spectra associated with components of the mixture, wherein the components of the mixture include one or more components of the target mixture;
• receive, from the mid-infrared waveguide sensor, a target absorption spectrum of the target mixture;
• decompose the target absorption spectrum into target component absorption spectra using the trained machine learning model, each of the target component absorption spectra being associated with a corresponding component of the target mixture and comprises a predetermined number of data points across measured wavelengths of the target absorption spectrum;
• receive, from the mid-infrared waveguide sensor, measured absorption spectra of each of the components of the target mixture, the measured absorption spectra include a series of measured absorption spectra of varying concentrations of a respective component;
• apply linear fitting to the measured absorption spectra of each of the components of the target mixture to determine predetermined wavelengths of the measured absorption spectra for comparison;
• and
• compare each of the target component absorption spectra of the target mixture with the measured absorption spectra of a corresponding component at the predetermined wavelengths to determine a predicted component concentration for each of the components of the target mixture, wherein the predicted component concentration is associated with a concentration of the corresponding component having the measured absorption spectrum that best fits the target component absorption spectrum of the corresponding component of the target mixture at the predetermined wavelengths.

A computer-implemented method for determining predicted component concentrations of a target mixture, the method comprising:

• receiving, from a mid-infrared waveguide sensor, first training absorption spectra for a plurality of training mixtures, the plurality of training mixtures each having one or more components associated with the target mixture and comprises different component concentrations;
• training a first machine learning model using the first training absorption spectra to obtain a first trained machine learning model, the first trained machine learning model being adapted to classify an absorption spectrum of a mixture having one or more of the components associated with the target mixture to identify specific component concentrations of the mixture, the identified specific component concentrations being one of the different predetermined component concentrations;
• receiving, from the mid-infrared waveguide sensor, a target absorption spectrum of the target mixture;
• and
• determining the predicted component concentrations of the target mixture by classifying the target absorption spectrum using the first trained machine learning model.