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. 2022 Oct 5;12(10):945.
doi: 10.3390/metabo12100945.

Hopomics: Humulus lupulus Brewing Cultivars Classification Based on LC-MS Profiling and Nested Feature Selection

Yuriy Andreevich Ikhalaynen  1 , Ivan Victorovich Plyushchenko  1 , Igor Alexandrovich Rodin  1   2
Affiliations

Hopomics: Humulus lupulus Brewing Cultivars Classification Based on LC-MS Profiling and Nested Feature Selection

Yuriy Andreevich Ikhalaynen et al. Metabolites. .

Abstract

Omics approaches in plant analysis find many different applications, from classification to new bioactive compounds discovery. Metabolomics seems to be one of the most informative ways of describing plants' phenotypes, since commonly used methods such as liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance spectroscopy (NMR) could provide a huge amount of information about samples. However, due to high efficiency, many disadvantages arise with the complexity of the experimental design. In the present work, we demonstrate an untargeted metabolomics pipeline with the example of a Humulus lupulus classification task. LC-MS profiling of brewing cultivars samples was carried out as a starting point. Hierarchical cluster analysis (HCA)-based classification in combination with nested feature selection was provided for sample discrimination and marker compounds discovery. Obtained metabolome-based classification showed an expected difference compared to genetic-based classification data. Nine compounds were found to have the biggest classification power during nested feature selection. Using database search and molecular network construction, five of them were identified as known hops bitter compounds.

Keywords: Humulus lupulus; machine learning; metabolomics; untargeted profiling.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Quality assurance projections of study samples on principal components surface. “A” corresponds to the positive ion’s signals table, “B”—negative.
Figure 2
Figure 2
The results of PC-based HCA. The genetic origin of the samples is presented as the color of leaves and sample labels. Newly obtained groups are represented as a background color. Definitions of sample labels are provided in the Table S1.
Figure 3
Figure 3
Projection of study samples on principal components surface. Newly assigned groups are demonstrated by color, geographic origin is represented by points shape.
Figure 4
Figure 4
Example of batch sequence used in the experiment. Blank samples are marked as “Bl”.
Figure 5
Figure 5
Scheme of implemented nested feature selection algorithm.
See this image and copyright information in PMC

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