Raman Spectroscopy Deconvolution Using Machine Learning

Proper characterization of a tumor is essential for informing treatment and assessing prognosis for the patient. The concentration of certain biomarkers, presence or absence of specific mutations, and even the pattern of distribution of biomarkers throughout the tumor can be extremely important in determining the aggressiveness of the tumor. For example, tumors that are more homogenous tend to be less aggressive and have a better prognosis than those that are more heterogenous. Therefore, we seek to image multiple biomarkers in vivo using targeted dyes.
However, imaging the tumor multiple times in series without an invasive biopsy is much too time consuming. Usually, for these targeted dyes, imaging occurs 5-7 days after the imaging agent is administered, meaning that imaging any more than one or two biomarkers would be prohibitively lengthy, possibly affecting timely treatment of the tumor. Therefore, we can utilize unique surface-enhanced resonance Ra- man scattering (SERRS) particles to target distinct biomarkers. In order to determine the concentration of each particle at each point, the individual spectra need to be deconvolved from the multiplexed spectra.
Conventional methods for separating the spectra, nonnegative least squares (NNLS), have been successful for low numbers of spectra. However, NNLS must calculate the pseudoinverse, and as the number of spectra increases, the condition number for that matrix increases quickly. Thus, past five spectra or so, using NNLS to deconvolute the spectra becomes untenable. Thus, we aim to use machine learning as an alternative to NNLS, with the potential to expand spectral deconvolution to more spectra accurately and quickly during run time.