An efficient label-free analyte detection algorithm for time-resolved spectroscopy
This addresses the bottleneck of expert reliance in spectroscopy for applications like biomedicine, though it appears incremental as it builds on existing dimensionality-reduction methods.
The paper tackles the problem of label-free analyte detection in time-resolved spectroscopy by proposing a novel unsupervised machine learning algorithm, achieving effectiveness in detecting amino acids in LC-Raman spectroscopy.
Time-resolved spectral techniques play an important analysis tool in many contexts, from physical chemistry to biomedicine. Customarily, the label-free detection of analytes is manually performed by experts through the aid of classic dimensionality-reduction methods, such as Principal Component Analysis (PCA) and Non-negative Matrix Factorization (NMF). This fundamental reliance on expert analysis for unknown analyte detection severely hinders the applicability and the throughput of these such techniques. For this reason, in this paper, we formulate this detection problem as an unsupervised learning problem and propose a novel machine learning algorithm for label-free analyte detection. To show the effectiveness of the proposed solution, we consider the problem of detecting the amino-acids in Liquid Chromatography coupled with Raman spectroscopy (LC-Raman).