Issue 6, 2023

Application of deep learning to support peak picking during non-target high resolution mass spectrometry workflows in environmental research

Abstract

With the advent of high-resolution mass spectrometry (HRMS), untargeted analytical approaches have become increasingly important across many different disciplines including environmental fields. However, analysing mass spectra produced by HRMS can be challenging due to the sensitivity of low abundance analytes, the complexity of sample matrices and the volume of data produced. This is further compounded by the challenge of using pre-processing algorithms to reliably extract useful information from the mass spectra whilst removing experimental artefacts and noise. It is essential that we investigate innovative technology to overcome these challenges and improve analysis in this data-rich area. The application of artificial intelligence to support data analysis in HRMS has a strong potential to improve current approaches and maximise the value of generated data. In this work, we investigated the application of a deep learning approach to classify MS peaks shortlisted by pre-processing workflows. The objective was to classify extracted ROIs into one of three classes to sort feature lists for downstream data interpretation. We developed and compared several convolutional neural networks (CNN) for peak classification using the Python library Keras. The optimized CNN demonstrated an overall accuracy of 85.5%, a sensitivity of 98.8% and selectively of 97.8%. The CNN approach rapidly and accurately classified peaks, reducing time and costs associated with manual curation of shortlisted features after peak picking. This will further support interpretation and understanding from this discovery-driven area of analytical science.

Graphical abstract: Application of deep learning to support peak picking during non-target high resolution mass spectrometry workflows in environmental research

Supplementary files

Article information

Article type
Paper
Submitted
12 Janv. 2023
Accepted
12 Apr. 2023
First published
19 Apr. 2023
This article is Open Access
Creative Commons BY license

Environ. Sci.: Adv., 2023,2, 877-885

Application of deep learning to support peak picking during non-target high resolution mass spectrometry workflows in environmental research

K. Mottershead and T. H. Miller, Environ. Sci.: Adv., 2023, 2, 877 DOI: 10.1039/D3VA00005B

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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