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. 2021 Jun 1;11:11460. doi: 10.1038/s41598-021-90961-6

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© The Author(s) 2021

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 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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(a) OPLS-DA score Plot. t1: Predicted principal components-score value of main components and difference between observation groups, t01: Orthogonal principal component-score value of orthogonal components and difference in observation group, R²Y: Percentage of Y matrix information that can be explained by OPLS-DA, Q²Y (Q²): Prediction ability of OPLS-DA, Pre: Predicted principal component number, RMSEE: Root mean squared error, Red: ZP, Blue: YS. (b) OPLS-DA permutation verification plot. Red: Q²Y, Blue: R²Y. OPLS-DA score Plot visualized in ggplot2 toolkit of R v1.0.1 (https://www.r-project.org/).