Machine learning methods are used to find patterns within preferably large datasets, such as those from Raman spectral acquisitions; however, the generation of these large datasets is time consuming, and the data manipulation is cumbersome. This work seeks to circumvent these issues by determining if experimental data can be fortified or substituted with simulated data. To explore how machine learning can be used to accelerate the generation of spectroscopic data sets, Raman spectra were collected on a variety of different molecules in solutions made of 2-3 chemical species. These spectra were then used to simulate data with the same relative concentrations using Reverse Self Modeling Curve Resolution (RSMCR). These data sets were used to train regression models using nonlinear regression and artificial neural network approaches. The performance of these models was validated using previously withheld experimental Raman spectral data to determine how well each dataset worked as a generalizable basis for the regression model.

Regression-based machine learning quantification of experimental Raman spectra through reverse self modeling curve resolution

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