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. 2024 Aug 27;14:19865. doi: 10.1038/s41598-024-68345-3

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© The Author(s) 2024, corrected publication 2024

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, 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 you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. 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-nc-nd/4.0/.

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PCA and PLS-DA analysis; (a) Principal component analysis PCA score plot showing distinct relationship between samples and differences in resistant cultivar inoculated with the pathogen LM (RP) vs resistant cultivar with mock inoculation (RM) at 48 hpi/21 °C in negative ionization mode with the MetaboAnalystR 6.0 software package along the x-axis (PC1) and within groups along the y-axis (PC2). PC1 and PC2 respectively summarize 29.8% and 27.1% of the observed variance in the metabolites, green and red shaded areas are the 95% confidence regions of each group; (b) Scores plot of metabolite features in PLS-DA (partial least squares-discriminant analysis) model representing covariance between component 1 (25.8%) at x-axis and component 2 (26.5%) at y-axis; (c) Variable of Importance in Projection (VIP) score plots derived from PLS-DA analysis, a higher VIP score (> 2.0) for metabolites represent higher contribution in the PLS-DA model.