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Metabolites
Volume 12
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10.3390/metabo12100993
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Article

Analysis of Primary Liquid Chromatography Mass Spectrometry Data by Neural Networks for Plant Samples Classification

by
Polina Turova
1,*,
Andrey Stavrianidi
1,2,
Viktor Svekolkin
3,
Dmitry Lyskov
4,
Ilya Podolskiy
5,
Igor Rodin
1,
Oleg Shpigun
1,2 and
Aleksey Buryak
2
1
Faculty of Chemistry, M.V. Lomonosov Moscow State University, 1-3 Leninskie Gory, Moscow 119991, Russia
2
A.N. Frumkin Institute of Physical Chemistry and Electrochemistry, Russian Academy of Sciences, 31-4 Leninsky Prospect, Moscow 119071, Russia
3
BostonGene Corporation, University Office Park III, 95 Sawyer Road, Waltham, MA 02453, USA
4
Faculty of Biology, M.V. Lomonosov Moscow State University, 1-12 Leninskie Gory, Moscow 119234, Russia
5
Bruker Ltd., Pyatnitskaya 50/2 Build. 1, Moscow 119017, Russia
*
Author to whom correspondence should be addressed.
Metabolites 2022, 12(10), 993; https://doi.org/10.3390/metabo12100993
Submission received: 15 September 2022 / Revised: 11 October 2022 / Accepted: 17 October 2022 / Published: 19 October 2022
(This article belongs to the Special Issue Advances in Metabolic Studies in Plant Extraction)
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Abstract

Plant samples are potential sources of physiologically active secondary metabolites and their classification is an extremely important task in traditional medicine and other fields of research. In the production of herbal drugs, different plant parts of the same or related species can serve as adulterants for primary plant material. The use of highly informative and relatively easily accessible tools, such as liquid chromatography and low-resolution mass spectrometry, helps to solve these tasks by means of fingerprint analysis. In this study, to reveal specific plant part features for 20 species from one family (Apiaceae), and to preserve the maximum information content, two approaches are suggested. In both cases, minimal raw data pretreatment, including rescaling of time and m/z axes and cutting off some uninformative regions, was applied. For the support vector machine (SVM) method, tensor unfolding was required, while neural networks (NNs) were able to work directly with squared heatmaps as input data. Moreover, five data augmentation variants are proposed, to overcome the typical problem of a lack of data. As a result, a comparable F1-score close to 0.75 was achieved by SVM and two employed NN architectures. Eight marker compounds belonging to chlorophylls, lipids, and coumarin apio-glucosides were tentatively identified as characteristic of their corresponding sample groups: roots, stems, leaves, and fruits. The proposed approaches are simple, information-saving and can be applied to a broad type of tasks in metabolomics.
Keywords: Apiaceae; raw LC-MS data; neural networks; support vector machine; augmentation Apiaceae; raw LC-MS data; neural networks; support vector machine; augmentation

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MDPI and ACS Style

Turova, P.; Stavrianidi, A.; Svekolkin, V.; Lyskov, D.; Podolskiy, I.; Rodin, I.; Shpigun, O.; Buryak, A. Analysis of Primary Liquid Chromatography Mass Spectrometry Data by Neural Networks for Plant Samples Classification. Metabolites 2022, 12, 993. https://doi.org/10.3390/metabo12100993

AMA Style

Turova P, Stavrianidi A, Svekolkin V, Lyskov D, Podolskiy I, Rodin I, Shpigun O, Buryak A. Analysis of Primary Liquid Chromatography Mass Spectrometry Data by Neural Networks for Plant Samples Classification. Metabolites. 2022; 12(10):993. https://doi.org/10.3390/metabo12100993

Chicago/Turabian Style

Turova, Polina, Andrey Stavrianidi, Viktor Svekolkin, Dmitry Lyskov, Ilya Podolskiy, Igor Rodin, Oleg Shpigun, and Aleksey Buryak. 2022. "Analysis of Primary Liquid Chromatography Mass Spectrometry Data by Neural Networks for Plant Samples Classification" Metabolites 12, no. 10: 993. https://doi.org/10.3390/metabo12100993

APA Style

Turova, P., Stavrianidi, A., Svekolkin, V., Lyskov, D., Podolskiy, I., Rodin, I., Shpigun, O., & Buryak, A. (2022). Analysis of Primary Liquid Chromatography Mass Spectrometry Data by Neural Networks for Plant Samples Classification. Metabolites, 12(10), 993. https://doi.org/10.3390/metabo12100993

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