  Accurate detection and analysis of traces of persistent organic pollutants in
water is important in many areas, including environmental monitoring and food
quality control, due to their long environmental stability and potential
bioaccumulation. While conventional analysis of organic pollutants requires
expensive equipment, surface enhanced Raman spectroscopy (SERS) has
demonstrated great potential for accurate detection of these contaminants.
However, SERS analytical difficulties, such as spectral preprocessing,
denoising, and substrate-based spectral variation, have hindered widespread use
of the technique. Here, we demonstrate an approach for predicting the
concentration of sample pollutants from messy, unprocessed Raman data using
machine learning. Frequency domain transform methods, including the Fourier and
Walsh Hadamard transforms, are applied to sets of Raman spectra of three model
micropollutants in water (rhodamine 6G, chlorpyrifos, and triclosan), which are
then used to train machine learning algorithms. Using standard machine learning
models, the concentration of sample pollutants are predicted with more than 80
percent cross-validation accuracy from raw Raman data. cross-validation
accuracy of 85 percent was achieved using deep learning for a moderately sized
dataset (100 spectra), and 70 to 80 percent cross-validation accuracy was
achieved even for very small datasets (50 spectra). Additionally, standard
models were shown to accurately identify characteristic peaks via analysis of
their importance scores. The approach shown here has the potential to be
applied to facilitate accurate detection and analysis of persistent organic
pollutants by surface-enhanced Raman spectroscopy.
