Systems and methods to analyze multimodal spectroscopy data to profile samples

A non-transitory, processor-readable medium storing instructions that, when executed by a processor, cause the processor to:

• receive spectrum data associated with a sample;
• provide the spectrum data as input to a first machine learning model to produce dominant spectral variation data having a lower dimensionality than the spectrum data;
• provide the dominant spectral variation data as input to a second machine learning model to produce embedded data that represents at least one feature of the spectrum data;
• and
• provide the embedded data as input to a third machine learning model to classify the sample.

A non-transitory, processor-readable medium storing instructions that, when executed by a processor, cause the processor to:

• receive a plurality of sets of spectrum data, each set of spectrum data associated with (1) a sample and (2) a spectrum different from remaining spectra from a plurality of spectra;
• generate a model based on at least one set of spectrum data from the plurality of sets of spectrum data;
• perform spectral deconvolution on at least one set of spectrum data from the plurality of sets of spectrum data by:
• convolving the model based on at least one of a point spread function associated with the at least one set of spectrum data or an impulse response associated with the at least one set of spectrum data, to produce a convoluted model, and
• producing deconvoluted spectrum data based on the convoluted model;
• provide the deconvoluted spectrum data as input to a multimodal fusion network having a cross-attention layer to predict a cross-mode feature associated with the plurality of sets of spectrum data;
• and
• determine a composition of the sample based on the cross-mode feature.