An efficient label-free analyte detection algorithm for time-resolved spectroscopy

TL;DR

This paper introduces a novel unsupervised machine learning algorithm for label-free analyte detection in time-resolved spectroscopy, improving automation and throughput over traditional expert-dependent methods.

Contribution

The paper formulates analyte detection as an unsupervised learning problem and proposes a new algorithm, enhancing automation in spectral analysis.

Findings

Effective detection of amino acids in LC-Raman spectroscopy

Outperforms traditional PCA and NMF methods

Reduces reliance on expert analysis

Abstract

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).