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. 2021 May 17;16:7. doi: 10.1186/s12263-021-00688-2

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© The Author(s) 2021

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 8 — Identification of metabolites using Sparse multilevel partial least squares-discriminant analysis (smPLS-DA). A bi-dimensional score plot is shown on the left panel. The score plot reveals the distinct blood metabolomic profile between pre- (red dots) and post-supplementation (green dots) paired participants. The two principal components of the smPLS-DA model along with their corresponding variance in group discrimination are shown on y- and x-axes, respectively. The loading plot representing the top 10 metabolites selected on the first component of the smPLS-DA model is shown on the right. Horizontal bars represent the loading weights of each metabolite. Most important metabolites in group discrimination are ordered according to their loading weights, from bottom to top. Bar colour represents either an increase following supplementation (green bars) or decrease following supplementation (red bars). TG triglyceride, Cer ceramide, DG diacylglycerol, SM sphingolipid, Orn ornithine