5 research outputs found
Predictors of fruit and vegetable consumption among Swedish speaking schoolchildren in the capital region of Finland
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
A New Bayesian Approach for Estimating the Presence of a Suspected Compound in Routine Screening Analysis
A novel method for compound identification in liquid chromatography-high resolution mass spectrometry (LC-HRMS) is proposed. The method, based on Bayesian statistics, accommodates all possible uncertainties involved, from instrumentation up to data analysis into a single model yielding the probability of the compound of interest being present/absent in the sample. This approach differs from the classical methods in two ways. First, it is probabilistic (instead of deterministic); hence, it computes the probability that the compound is (or is not) present in a sample. Second, it answers the hypothesis “the compound is present”, opposed to answering the question “the compound feature is present”. This second difference implies a shift in the way data analysis is tackled, since the probability of interfering compounds (i.e., isomers and isobaric compounds) is also taken into account
Application of Fragment Ion Information as Further Evidence in Probabilistic Compound Screening Using Bayesian Statistics and Machine Learning : A Leap Toward Automation
In this work, we introduce an automated, efficient, and elegant model to combine all pieces of evidence (e.g., expected retention times, peak shapes, isotope distributions, fragment-to-parent ratio) obtained from liquid chromatography-tandem mass spectrometry (LC-MS/MS/MS) data for screening purposes. Combining all these pieces of evidence requires a careful assessment of the uncertainties in the analytical system as well as all possible outcomes. To-date, the majority of the existing algorithms are highly dependent on user input parameters. Additionally, the screening process is tackled as a deterministic problem. In this work we present a Bayesian framework to deal with the combination of all these pieces of evidence. Contrary to conventional algorithms, the information is treated in a probabilistic way, and a final probability assessment of the presence/absence of a compound feature is computed. Additionally, all the necessary parameters except the chromatographic band broadening for the method are learned from the data in training and learning phase of the algorithm, avoiding the introduction of a large number of user-defined parameters. The proposed method was validated with a large data set and has shown improved sensitivity and specificity in comparison to a threshold-based commercial software package.</p