1 research outputs found
Artificial Neural Network for Probabilistic Feature Recognition in Liquid Chromatography Coupled to High-Resolution Mass Spectrometry
In this work, a novel
probabilistic untargeted feature detection
algorithm for liquid chromatography coupled to high-resolution mass
spectrometry (LC–HRMS) using artificial neural network (ANN)
is presented. The feature detection process is approached as a pattern
recognition problem, and thus, ANN was utilized as an efficient feature
recognition tool. Unlike most existing feature detection algorithms,
with this approach, any suspected chromatographic profile (i.e., shape
of a peak) can easily be incorporated by training the network, avoiding
the need to perform computationally expensive regression methods with
specific mathematical models. In addition, with this method, we have
shown that the high-resolution raw data can be fully utilized without
applying any arbitrary thresholds or data reduction, therefore improving
the sensitivity of the method for compound identification purposes.
Furthermore, opposed to existing deterministic (binary) approaches,
this method rather estimates the probability of a feature being present/absent
at a given point of interest, thus giving chance for all data points
to be propagated down the data analysis pipeline, weighed with their
probability. The algorithm was tested with data sets generated from
spiked samples in forensic and food safety context and has shown promising
results by detecting features for all compounds in a computationally
reasonable time