Scientific instrument support systems and methods for mitigating spectral drift

One or more non-transitory computer readable media comprising instructions thereon that, when executed by one or more processing devices of a scientific instrument support apparatus, cause the scientific instrument support apparatus to:

• obtain a spectrum recorded with an optical emission spectrometer and input data comprising one or more condition parameters indicative of an operating condition of the optical emission spectrometer or an environment of the optical emission spectrometer at a time of recording the spectrum with the optical emission spectrometer;
• apply the input data as an input to a machine learning model that is configured to output, in response to the input data, output data comprising one or more transform parameters of a transformation to be applied to the obtained spectrum to mitigate distortion of the spectrum due to a discrepancy between the operating condition and a baseline operating condition;
• obtain one or more transform parameters as an output of the machine learning model;
• and
• apply the transformation in accordance with the obtained one or more transform parameters to the obtained spectrum to mitigate distortion of the spectrum due to a discrepancy between the operating condition and the baseline operating condition.

A method of obtaining training data for training a machine learning model, the method comprising:

• recording a plurality of optical emission spectra of a reference analyte with an optical emission spectrometer for respective operating conditions of the optical emission spectrometer;
• storing input data for the machine learning model, the input data comprising, for each recorded optical emission spectrum, one or more parameters indicative of the respective operating condition;
• for each recorded optical emission spectrum, adjusting one or more transform parameters of a transform to register the optical emission spectrum to a baseline optical emission spectrum of the reference analyte using the transform, wherein the baseline optical emission spectrum was recorded for a baseline operating condition;
• and
• storing output data for the machine learning model comprising, for each optical emission spectrum, the respective adjusted one or more transform parameters in association with the respective input data for each optical emission spectrum as a training data pair.