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. 2020 Feb 18;10:2820. doi: 10.1038/s41598-020-59840-4

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© The Author(s) 2020

Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

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Hierarchical clustering and principal component analysis (PCA) to understand variable associations in treated tomato plants. CBEs were associated with five major clusters. (a) The entire data were analysed using PCA. The variables included root DW (root dry weight), shoot DW (shoot dry weight), Root length, Shoot length, Chl a (chlorophyll a), Chl b (chlorophyll b), Carotenoids (Carotenoids), Nitrogen, Phosphorus and Potassium. The lines originating from central point of PCA plot indicate positive or negative correlations of different variables (b); where their closeness indicates correlation strength with particular treatment S3.