1 research outputs found
Determination of Trace Organic Contaminant Concentration via Machine Classification of Surface-Enhanced Raman Spectra
Surface-enhanced Raman spectroscopy
(SERS) has been well explored
as a highly effective characterization technique that is capable of
chemical pollutant detection and identification at very low concentrations.
Machine learning has been previously used to identify compounds based
on SERS spectral data. However, utilization of SERS to quantify concentrations,
with or without machine learning, has been difficult due to the spectral
intensity being sensitive to confounding factors such as the substrate
parameters, orientation of the analyte, and sample preparation technique.
Here, we demonstrate an approach for predicting the concentration
of sample pollutants from SERS spectra using machine learning. Frequency
domain transform methods, including the Fourier and Walsh–Hadamard
transforms, are applied to spectral data sets of three analytes (rhodamine
6G, chlorpyrifos, and triclosan), which are then used to train machine
learning algorithms. Using standard machine learning models, the concentration
of the sample pollutants is predicted with >80% cross-validation
accuracy
from raw SERS data. A cross-validation accuracy of 85% was achieved
using deep learning for a moderately sized data set (∼100 spectra),
and 70–80% was achieved for small data sets (∼50 spectra).
Performance can be maintained within this range even when combining
various sample preparation techniques and environmental media interference.
Additionally, as a spectral pretreatment, the Fourier and Hadamard
transforms are shown to consistently improve prediction accuracy across
multiple data sets. Finally, standard models were shown to accurately
identify characteristic peaks of compounds via analysis of their importance
scores, further verifying their predictive value